Devices, systems, and methods for learning and using artificially intelligent interactive memories

ABSTRACT

Aspects of the disclosure generally relate to computing devices and may be generally directed to devices, systems, methods, and/or applications for learning conversations among two or more conversation participants, storing this knowledge in a knowledgebase (i.e. neural network, graph, sequences, etc.), and enabling a user to simulate a conversation with an artificially intelligent conversation participant.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims priority under 35U.S.C. § 120 from, nonprovisional U.S. patent application Ser. No.15/016,280 entitled “DEVICES, SYSTEMS, AND METHODS FOR LEARNING ANDUSING ARTIFICIALLY INTELLIGENT INTERACTIVE MEMORIES”, filed on Feb. 5,2016. The disclosure of the foregoing document is incorporated herein byreference.

FIELD

The disclosure generally relates to electronic devices. The disclosureincludes devices, apparatuses, systems, and related methods forproviding advanced learning, anticipation, simulation, and/or otherfunctionalities to enable artificially intelligent interactive memories.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND

Still pictures are commonly used to record memories of persons orobjects. One of still picture's limitations is the fact that it is stilland that it provides no interactivity. Motion pictures are also commonlyused to record memories of persons or objects including the timedimension. One of motion picture's limitations is the fact that the onlyoperations a user can perform on a motion picture are playing, pausing,forwarding, and rewinding the sequence of pictures. Essentially, a usercan only watch persons or objects depicted in a still or motion picturewithout being able to interact with them. Still and motion pictures failto provide a way in which a user may want to experience content throughthe use of artificial intelligence on today's computing, mobile, and/orembedded devices.

SUMMARY OF THE INVENTION

In some aspects, the disclosure relates to a system for learningartificially intelligent interactive memories. The system may beimplemented at least in part on one or more computing devices. In someembodiments, the system comprises a server including one or moreprocessor circuits coupled to a memory unit. The system may furtherinclude a first computing device including a picture-capturing deviceconfigured to capture a stream of digital pictures of a firstconversation participant and include a sound-capturing device configuredto capture a stream of digital sound samples of the first conversationparticipant, the first computing device coupled to the server via anetwork. The system may further include a second computing deviceincluding a picture-capturing device configured to capture a stream ofdigital pictures of a second conversation participant and include asound-capturing device configured to capture a stream of digital soundsamples of the second conversation participant, the second computingdevice coupled to the server via the network. The one or more processorcircuits may be configured to detect the first conversationparticipant's first conversational activity from at least one of thestream of digital pictures of the first conversation participant or thestream of digital sound samples of the first conversation participant.The one or more processor circuits may be further configured to detectthe second conversation participant's first conversational activity fromat least one of the stream of digital pictures of the secondconversation participant or the stream of digital sound samples of thesecond conversation participant. The one or more processor circuits maybe further configured to generate a first round of conversationalexchange including a recording of the first conversation participant'sfirst conversational activity and a recording of the second conversationparticipant's first conversational activity. The one or more processorcircuits may be further configured to cause the memory unit to store thefirst round of conversational exchange, the first round ofconversational exchange being part of a stored plurality of rounds ofconversational exchange.

In certain embodiments, the picture-capturing device of the firstcomputing device or the picture-capturing device of the second computingdevice includes a motion picture camera. In further embodiments, thestream of digital pictures of the first conversation participantincludes the first conversation participant's visual expressions orcommunication and the stream of digital pictures of the secondconversation participant includes the second conversation participant'svisual expressions or communication. In further embodiments, the streamof digital pictures of the first conversation participant or the streamof digital pictures of the second conversation participant includes adigital motion picture. The digital motion picture may include a MPEGmotion picture, an AVI motion picture, a FLV motion picture, a MOVmotion picture, a RM motion picture, a SWF motion picture, a WMV motionpicture, a DivX motion picture, or a digitally encoded motion picture.In further embodiments, the stream of digital pictures of the firstconversation participant or the stream of digital pictures of the secondconversation participant includes or is associated with an extrainformation, the extra information comprising a time information, alocation information, an observed information, or a contextualinformation.

In some embodiments, the sound-capturing device of the first computingdevice or the sound-capturing device of the second computing deviceincludes a microphone. In further embodiments, the stream of digitalsound samples of the first conversation participant includes the firstconversation participant's verbal expressions or communication and thestream of digital sound samples of the second conversation participantincludes the second conversation participant's verbal expressions orcommunication, in further embodiments, the stream of digital soundsamples of the first conversation participant or the stream of digitalsound samples of the second conversation participant includes a digitalsound. The digital sound may include a WAV digital sound, a WMA digitalsound, an AIFF digital sound, a MP3 digital sound, a RA digital sound, aOGG digital sound, or a digitally encoded sound. In further embodiments,the stream of digital sound samples of the first conversationparticipant or the stream of digital sound samples of the secondconversation participant includes or is associated with an extrainformation, the extra information comprising a time information, alocation information, an observed information, or a contextualinformation. In further embodiments, the stream of digital pictures ofthe first conversation participant and the stream of digital soundsamples of the first conversation participant are capturedsimultaneously. In further embodiments, the stream of digital picturesof the second conversation participant and the stream of digital soundsamples of the second conversation participant are capturedsimultaneously. In further embodiments, the stream of digital picturesof the first conversation participant, the stream of digital soundsamples of the first conversation participant, the stream of digitalpictures of the second conversation participant, and the stream ofdigital sound samples of the second conversation participant arecaptured simultaneously.

In certain embodiments, the first conversation participant's firstconversational activity or the second conversation participant's firstconversational activity includes at least one of: a speaking, a silentfacial expression, a silent body movement, a motionless silence, anabsence from the conversation, or a conversational action. In furtherembodiments, the first conversation participant's first conversationalactivity includes a first conversation participant's speaking and thesecond conversation participant's first conversational activity includesa second conversation participant's silent facial expression; a secondconversation participant's silent body movement, a second conversationparticipant's motionless silence, or a second conversation participant'sabsence from the conversation. In further embodiments, the detecting thefirst conversation participant's speaking includes recognizing the firstconversation participant's speech in the stream of digital sound samplesof the first conversation participant. In further embodiments, thedetecting the first conversation participant's speaking includesdetermining a beginning and an end of the first conversationparticipant's speaking. The determining the beginning of the firstconversation participant's speaking may include recognizing the firstconversation participant's speech after a threshold period of silence inthe stream of digital sound samples of the first conversationparticipant. The determining the end of the first conversationparticipant's speaking may include recognizing a threshold period ofsilence after the first conversation participant's speech in the streamof digital sound samples of the first conversation participant. Infurther embodiments, the detecting the second conversation participant'ssilent facial expression includes recognizing the second conversationparticipant's facial expression in the stream of digital pictures of thesecond conversation participant and recognizing the second conversationparticipant's silence in the stream of digital sound samples of thesecond conversation participant. In further embodiments, the detectingthe second conversation participant's silent body movement includesrecognizing the second conversation participant's body movement in thestream of digital pictures of the second conversation participant andrecognizing the second conversation participant's silence in the streamof digital sound samples of the second conversation participant. Infurther embodiments, the detecting the second conversation participant'smotionless silence includes recognizing no motion or a marginal motionof the second conversation participant in the stream of digital picturesof the second conversation participant and recognizing the secondconversation participant's silence in the stream of digital soundsamples of the second conversation participant. The marginal motion ofthe second conversation participant may include a motion of the secondconversation participant that does not exceed a threshold for motion. Infurther embodiments, the detecting the second conversation participant'sabsence from the conversation includes recognizing the secondconversation participant's absence in the stream of digital pictures ofthe second conversation participant.

In some embodiments, the first conversation participant's firstconversational activity includes a first conversation participant'ssilent facial expression, a first conversation participant's silent bodymovement, a first conversation participant's motionless silence, or afirst conversation participant's absence from the conversation and thesecond conversation participant's first conversational activity includesa second conversation participant's speaking. In further embodiments,the first conversation participant's first conversational activityincludes a first conversation participant's speaking and the secondconversation participant's first conversational activity includes asecond conversation participant's speaking. In further embodiments, thefirst conversation participant's first conversational activity includesa first conversation participant's silent facial expression, a firstconversation participant's silent body movement, a first conversationparticipant's motionless silence, or a first conversation participant'sabsence from the conversation and the second conversation participant'sfirst conversational activity includes a second conversationparticipant's silent facial expression, a second conversationparticipant's silent body movement, a second conversation participant'smotionless silence, or a second conversation participant's absence fromthe conversation. In further embodiments, the timing of the firstconversation participant's first conversational activity coincides,partially coincides, or overlaps with the timing of the secondconversation participant's first conversational activity.

In certain embodiments, the detecting the first conversationparticipant's first conversational activity includes recognizing thefirst conversation participant's visual and verbal expressions orcommunication in a first part of a conversation. In further embodiments,wherein the detecting the first conversation participant's firstconversational activity includes identifying a first sub-stream of thestream of digital pictures of the first conversation participant,wherein the first sub-stream of the stream of digital pictures of thefirst conversation participant comprises the first conversationparticipant's visual expressions or communication in a first part of aconversation. In further embodiments, the detecting the firstconversation participant's first conversational activity includesidentifying a first sub-stream of the stream of digital sound samples ofthe first conversation participant, wherein the first sub-stream of thestream of digital sound samples of the first conversation participantcomprises the first conversation participant's verbal expressions orcommunication in a first part of a conversation. In further embodiments,the detecting the second conversation participant's first conversationalactivity includes recognizing the second conversation participant'svisual and verbal expressions or communication in a first part of aconversation. In further embodiments, the detecting the secondconversation participant's first conversational activity includesidentifying a first sub-stream of the stream of digital pictures of thesecond conversation participant, wherein the first sub-stream of thestream of digital pictures of the second conversation participantcomprises the second conversation participant's visual expressions orcommunication in a first part of a conversation. In further embodiments,the detecting the second conversation participant's first conversationalactivity includes identifying a first sub-stream of the stream ofdigital sound samples of the second conversation participant, whereinthe first sub-stream of the stream of digital sound samples of thesecond conversation participant comprises the second conversationparticipant's verbal expressions or communication in a first part of aconversation. In further embodiments, the detecting the firstconversation participant's first conversational activity includesrecognizing the first conversation participant's speech or sound in thestream of digital sound samples of the first conversation participant,in further embodiments, the detecting the second conversationparticipant's first conversational activity includes recognizing thesecond conversation participant's speech or sound in the stream ofdigital sound samples of the second conversation participant. In furtherembodiments, the detecting the first conversation participant's firstconversational activity includes recognizing the first conversationparticipant's face or body part in the stream of digital pictures of thefirst conversation participant. In further embodiments, the detectingthe second conversation participant's first conversational activityincludes recognizing the second conversation participant's face or bodypart in the stream of digital pictures of the second conversationparticipant. In further embodiments, the detecting the firstconversation participant's first conversational activity includesdetermining a beginning and an end of the first conversationparticipant's first conversational activity, in further embodiments, thedetecting the second conversation participant's first conversationalactivity includes determining a beginning and an end of the secondconversation participant's first conversational activity.

In some embodiments, the recording of the first conversationparticipant's first conversational activity includes the firstconversation participant's visual expressions or communication in afirst part of a conversation and the first conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the firstconversation participant's first conversational activity includes afirst sub-stream of the stream of digital pictures of the firstconversation participant and a first sub-stream of the stream of digitalsound samples of the first conversation participant. The firstsub-stream of the stream of digital pictures of the first conversationparticipant may comprise the first conversation participant's visualexpressions or communication in a first part of a conversation and thefirst sub-stream of the stream of digital sound samples of the firstconversation participant comprises the first conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the secondconversation participant's first conversational activity includes thesecond conversation participant's visual expressions or communication ina first part of a conversation and the second conversation participant'sverbal expressions or communication in the first part of theconversation, in further embodiments, the recording of the secondconversation participant's first conversational activity includes afirst sub-stream of the stream of digital pictures of the secondconversation participant and a first sub-stream of the stream of digitalsound samples of the second conversation participant. The firstsub-stream of the stream of digital pictures of the second conversationparticipant may comprise the second conversation participant's visualexpressions or communication in a first part of a conversation and thefirst sub-stream of the stream of digital sound samples of the secondconversation participant comprises the second conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the firstconversation participant's first conversational activity or therecording of the second conversation participant's first conversationalactivity includes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information.

In certain embodiments, the first round of conversational exchangeincludes a unit of knowledge of how the first conversation participantacted relative to the second conversation participant in a first part ofa conversation and how the second conversation participant actedrelative to the first conversation participant in the first part of theconversation. In further embodiments, the first round of conversationalexchange includes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information, in furtherembodiments, the recording of the first conversation participant's firstconversational activity is correlated with the recording of the secondconversation participant's first conversational activity.

In some embodiments, the stored plurality of rounds of conversationalexchange are organized into at least one of: a neural network, a graph,a collection of sequences, a sequence, a knowledgebase, a knowledgestructure, or a data structure. In further embodiments, each round ofconversational exchange of the stored plurality of rounds ofconversational exchange is included in a neuron, a node, a vertex, or anelement of a neural network, a graph, a collection of sequences, asequence, a knowledgebase, a knowledge structure, or a data structure.In further embodiments, some rounds of conversational exchange of thestored plurality of rounds of conversational exchange areinterconnected.

In certain embodiments, the one or more processor circuits may befurther configured to: compare the first round of conversationalexchange with the stored plurality of rounds of conversational exchange,and determine that the stored plurality of rounds of conversationalexchange do not include a round of conversational exchange whosesimilarity with the first round of conversational exchange exceeds asimilarity threshold.

In some embodiments, the one or more processor circuits may be furtherconfigured to detect the first conversation participant's secondconversational activity from at least one of the stream of digitalpictures of the first conversation participant or the stream of digitalsound samples of the first conversation participant. The one or moreprocessor circuits may be further configured to detect the secondconversation participant's second conversational activity from at leastone of the stream of digital pictures of the second conversationparticipant or the stream of digital sound samples of the secondconversation participant. The one or more processor circuits may befurther configured to generate a second round of conversational exchangeincluding the First conversation participant's second conversationalactivity and the second conversation participant's second conversationalactivity. The one or more processor circuits may be further configuredto cause the memory unit to store the second round of conversationalexchange, the second round of conversational exchange being part of thestored plurality of rounds of conversational exchange.

In some embodiments, the one or more processor circuits may be furtherconfigured to create a connection between the stored first round ofconversational exchange and the stored second round of conversationalexchange. In further embodiments, the connection between the storedfirst round of conversational exchange and the stored second round ofconversational exchange includes or is associated with at least one of:an occurrence count, a weight, a parameter, or a data. In furtherembodiments, the stored plurality of rounds of conversational exchangeare organized into a neural network, and wherein the first round ofconversational exchange is stored into a first node of the neuralnetwork and the second round of conversational exchange is stored into asecond node of the neural network. The first node and the second nodemay be connected by a connection. The first node may be part of a firstlayer of the neural network and the second node may be part of a secondlayer of the neural network. In further embodiments, the storedplurality of rounds of conversational exchange are organized into agraph, and wherein the first round of conversational exchange is storedinto a first node of the graph and the second round of conversationalexchange is stored into a second node of the graph. The first node andthe second node may be connected by a connection. In furtherembodiments, the stored plurality of rounds of conversational exchangeare organized into a collection of sequences, and wherein the firstround of conversational exchange is stored into a first node of asequence of the collection of sequences and the second round ofconversational exchange is stored into a second node of the sequence ofthe collection of sequences. In further embodiments, the storedplurality of rounds of conversational exchange are organized into asequence, and wherein the first round of conversational exchange isstored into a first node of the sequence and the second round ofconversational exchange is stored into a second node of the sequence. Infurther embodiments, the one or more processor circuits may be furtherconfigured to: compare the second round of conversational exchange withthe stored plurality of rounds of conversational exchange, and determinethat the stored plurality of rounds of conversational exchange do notinclude a round of conversational exchange whose similarity with thesecond round of conversational exchange exceeds a similarity threshold.The recording of the first conversation participant's secondconversational activity may be correlated with the recording of thesecond conversation participant's second conversational activity.

In certain embodiments, the one or more processor circuits may befurther configured to update a connection between the stored first roundof conversational exchange and another round of conversational exchangeof the stored plurality of rounds of conversational exchange.

In some embodiments, the one or more processor circuits may be furtherconfigured to detect the first conversation participant's thirdconversational activity from at least one of the stream of digitalpictures of the first conversation participant or the stream of digitalsound samples of the first conversation participant. The one or moreprocessor circuits may be further configured to detect the secondconversation participant's third conversational activity from at leastone of the stream of digital pictures of the second conversationparticipant or the stream of digital sound samples of the secondconversation participant. The one or more processor circuits may befurther configured to generate a third round of conversational exchangeincluding a recording of the first conversation participant's thirdconversational activity and a recording of the second conversationparticipant's third conversational activity. The one or more processorcircuits may be further configured to compare the third round ofconversational exchange with the stored plurality of rounds ofconversational exchange. The one or more processor circuits may befurther configured to determine that the stored plurality of rounds ofconversational exchange include a round of conversational exchange whosesimilarity with the third round of conversational exchange exceeds asimilarity threshold. The one or more processor circuits may be furtherconfigured to update a connection between the stored first round ofconversational exchange and the round of conversational exchange whosesimilarity with the third round of conversational exchange exceeds asimilarity threshold. In further embodiments, the updating theconnection between the stored first round of conversational exchange andthe round of conversational exchange whose similarity with the thirdround of conversational exchange exceeds a similarity threshold includesupdating at least one of: an occurrence count, a weight, a parameter, ora data included in or associated with the connection. In furtherembodiments, the recording of the first conversation participant's thirdconversational activity is correlated with the recording of the secondconversation participant's third conversational activity.

In some embodiments, the one or more processor circuits may be furtherconfigured to filter the first conversation participant's face or bodypart from the stream of digital pictures of the first conversationparticipant. In further embodiments, the filtering the firstconversation participant's face or body part from the stream of digitalpictures of the first conversation participant includes retaining thefirst conversation participant's face or body part and removing aninsignificant content from the stream of digital pictures of the firstconversation participant.

In certain embodiments, the one or more processor circuits may befurther configured to filter the first conversation participant's speechor sound from the stream of digital sound samples of the firstconversation participant. In further embodiments, the filtering thefirst conversation participant's speech or sound from the stream ofdigital sound samples of the first conversation participant includesretaining the first conversation participant's speech or sound andremoving an insignificant sound from the stream of digital sound samplesof the first conversation participant.

In some aspects, the disclosure relates to a non-transitory computerstorage medium having a computer program stored thereon, the programcomprising instructions that when executed by one or more computingdevices cause the one or more computing devices to perform operationscomprising: capturing a stream of digital pictures of a firstconversation participant by a picture-capturing device of a firstcomputing device. The operations may further include capturing a streamof digital sound samples of the first conversation participant by asound-capturing device of the first computing device, the firstcomputing device coupled to a server via a network. The operations mayfurther include capturing a stream of digital pictures of a secondconversation participant by a picture-capturing device of a secondcomputing device. The operations may further include capturing a streamof digital sound samples of the second conversation participant by asound-capturing device of the second computing device, the secondcomputing device coupled to the server via the network. The operationsmay further include detecting the first conversation participant's firstconversational activity from at least one of the stream of digitalpictures of the first conversation participant or the stream of digitalsound samples of the first conversation participant. The operations mayfurther include detecting the second conversation participant's firstconversational activity from at least one of the stream of digitalpictures of the second conversation participant or the stream of digitalsound samples of the second conversation participant. The operations mayfurther include generating a first round of conversational exchangeincluding a recording of the first conversation participant's firstconversational activity and a recording of the second conversationparticipant's first conversational activity. The operations may furtherinclude storing the first round of conversational exchange into a memoryunit of the server, the first round of conversational exchange beingpart of a stored plurality of rounds of conversational exchange.

In some aspects, the disclosure relates to a method comprising: (a)capturing a stream of digital pictures of a first conversationparticipant by a picture-capturing device of a first computing device.The method may further include (b) capturing a stream of digital soundsamples of the first conversation participant by a sound-capturingdevice of the first computing device, the first computing device coupledto a server via a network. The method may further include (c) capturinga stream of digital pictures of a second conversation participant by apicture-capturing device of a second computing device. The method mayfurther include (d) capturing a stream of digital sound samples of thesecond conversation participant by a sound-capturing device of thesecond computing device, the second computing device coupled to theserver via the network The method may further include (e) detecting thefirst conversation participant's first conversational activity from atleast one of the stream of digital pictures of the first conversationparticipant or the stream of digital sound samples of the firstconversation participant, the detecting of (e) performed by one or moreprocessor circuits of the server The method may further include (f)detecting the second conversation participant's first conversationalactivity from at least one of the stream of digital pictures of thesecond conversation participant or the stream of digital sound samplesof the second conversation participant, the detecting of (f′) performedby the one or more processor circuits of the server The method mayfurther include (g) generating a first round of conversational exchangeincluding a recording of the first conversation participant's firstconversational activity and a recording of the second conversationparticipant's first conversational activity, the generating of (g)performed by the one or more processor circuits of the server The methodmay further include (h) storing the first round of conversationalexchange into a memory unit of the server, the first round ofconversational exchange being part of a stored plurality of rounds ofconversational exchange, the storing of (h) caused by the one or moreprocessor circuits of the server.

The operations or steps of the non-transitory computer storage mediumand/or the method may be performed by any of the elements of the abovedescribed system as applicable. The non-transitory computer storagemedium and/or the method may include any of the operations, steps, andembodiments of the above described system as applicable as well as thefollowing embodiments.

In some embodiments, the picture-capturing device of the first computingdevice or the picture-capturing device of the second computing deviceincludes a motion picture camera. In further embodiments, the stream ofdigital pictures of the first conversation participant includes thefirst conversation participant's visual expressions or communication andthe stream of digital pictures of the second conversation participantincludes the second conversation participant's visual expressions orcommunication. In further embodiments, the stream of digital pictures ofthe first conversation participant or the stream of digital pictures ofthe second conversation participant includes a digital motion picture.The digital motion picture may include a MPEG motion picture, an AVImotion picture, a FLV motion picture, a MOV motion picture, a RM motionpicture, a SWF motion picture, a WMV motion picture, a DivX motionpicture, or a digitally encoded motion picture. In further embodiments,the stream of digital pictures of the first conversation participant orthe stream of digital pictures of the second conversation participantincludes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information.

In certain embodiments, the sound-capturing device of the firstcomputing device or the sound-capturing device of the second computingdevice includes a microphone. In further embodiments, the stream ofdigital sound samples of the first conversation participant includes thefirst conversation participant's verbal expressions or communication andthe stream of digital sound samples of the second conversationparticipant includes the second conversation participant's verbalexpressions or communication. In further embodiments, the stream ofdigital sound samples of the first conversation participant or thestream of digital sound samples of the second conversation participantincludes a digital sound. The digital sound may include a WAV digitalsound, a WMA digital sound, an AIFF digital sound, a MP3 digital sound,a RA digital sound, a OGG digital sound, or a digitally encoded sound.In further embodiments, the stream of digital sound samples of the firstconversation participant or the stream of digital sound samples of thesecond conversation participant includes or is associated with an extrainformation, the extra information comprising a time information, alocation information, an observed information, or a contextualinformation. In further embodiments, the stream of digital pictures ofthe first conversation participant and the stream of digital soundsamples of the first conversation participant are capturedsimultaneously. In further embodiments, the stream of digital picturesof the second conversation participant and the stream of digital soundsamples of the second conversation participant are capturedsimultaneously. In further embodiments, the stream of digital picturesof the first conversation participant, the stream of digital soundsamples of the first conversation participant, the stream of digitalpictures of the second conversation participant, and the stream ofdigital sound samples of the second conversation participant arecaptured simultaneously.

In some embodiments, the first conversation participant's firstconversational activity or the second conversation participant's firstconversational activity includes at least one of: a speaking, a silentfacial expression, a silent body movement, a motionless silence, anabsence from the conversation, or a conversational action. In furtherembodiments, the first conversation participant's first conversationalactivity includes a first conversation participant's speaking and thesecond conversation participant's first conversational activity includesa second conversation participant's silent facial expression, a secondconversation participant's silent body movement, a second conversationparticipant's motionless silence, or a second conversation participant'sabsence from the conversation. In further embodiments, the detecting thefirst conversation participant's speaking includes recognizing the firstconversation participant's speech in the stream of digital sound samplesof the first conversation participant. In further embodiments, thedetecting the first conversation participant's speaking includesdetermining a beginning and an end of the first conversationparticipant's speaking. The determining the beginning of the firstconversation participant's speaking may include recognizing the firstconversation participant's speech after a threshold period of silence inthe stream of digital sound samples of the first conversationparticipant. The determining the end of the first conversationparticipant's speaking may include recognizing a threshold period ofsilence after the first conversation participant's speech in the streamof digital sound samples of the first conversation participant. Infurther embodiments, the detecting the second conversation participant'ssilent facial expression includes recognizing the second conversationparticipant's facial expression in the stream of digital pictures of thesecond conversation participant and recognizing the second conversationparticipant's silence in the stream of digital sound samples of thesecond conversation participant. In further embodiments, the detectingthe second conversation participant's silent body movement includesrecognizing the second conversation participant's body movement in thestream of digital pictures of the second conversation participant andrecognizing the second conversation participant's silence in the streamof digital sound samples of the second conversation participant. Infurther embodiments, the detecting the second conversation participant'smotionless silence includes recognizing no motion or a marginal motionof the second conversation participant in the stream of digital picturesof the second conversation participant and recognizing the secondconversation participant's silence in the stream of digital soundsamples of the second conversation participant. The marginal motion ofthe second conversation participant may include a motion of the secondconversation participant that does not exceed a threshold for motion. Infurther embodiments, the detecting the second conversation participant'sabsence from the conversation includes recognizing the secondconversation participant's absence in the stream of digital pictures ofthe second conversation participant. In further embodiments, the firstconversation participant's first conversational activity includes afirst conversation participant's silent facial expression, a firstconversation participant's silent body movement, a first conversationparticipant's motionless silence, or a first conversation participant'sabsence from the conversation and the second conversation participant'sfirst conversational activity includes a second conversationparticipant's speaking. In further embodiments, the first conversationparticipant's first conversational activity includes a firstconversation participant's speaking and the second conversationparticipant's first conversational activity includes a secondconversation participant's speaking. In further embodiments, the firstconversation participant's first conversational activity includes afirst conversation participant's silent facial expression, a firstconversation participant's silent body movement, a first conversationparticipant's motionless silence, or a first conversation participant'sabsence from the conversation and the second conversation participant'sfirst conversational activity includes a second conversationparticipant's silent facial expression, a second conversationparticipant's silent body movement, a second conversation participant'smotionless silence, or a second conversation participant's absence fromthe conversation, in further embodiments, the timing of the firstconversation participant's first conversational activity coincides,partially coincides, or overlaps with the timing of the secondconversation participant's first conversational activity.

In certain embodiments, the detecting the first conversationparticipant's first conversational activity includes recognizing thefirst conversation participant's visual and verbal expressions orcommunication in a first part of a conversation. In further embodiments,the detecting the first conversation participant's first conversationalactivity includes identifying a first sub-stream of the stream ofdigital pictures of the first conversation participant, wherein thefirst sub-stream of the stream of digital pictures of the firstconversation participant comprises the first conversation participant'svisual expressions or communication in a first part of a conversation.In further embodiments, the detecting the first conversationparticipant's first conversational activity includes identifying a firstsub-stream of the stream of digital sound samples of the firstconversation participant, wherein the first sub-stream of the stream ofdigital sound samples of the first conversation participant comprisesthe first conversation participant's verbal expressions or communicationin a first part of a conversation. In further embodiments, the detectingthe second conversation participant's first conversational activityincludes recognizing the second conversation participant's visual andverbal expressions or communication in a first part of a conversation.In further embodiments, the detecting the second conversationparticipant's first conversational activity includes identifying a firstsub-stream of the stream of digital pictures of the second conversationparticipant, wherein the first sub-stream of the stream of digitalpictures of the second conversation participant comprises the secondconversation participant's visual expressions or communication in afirst part of a conversation. In further embodiments, the detecting thesecond conversation participant's first conversational activity includesidentifying a first sub-stream of the stream of digital sound samples ofthe second conversation participant, wherein the first sub-stream of thestream of digital sound samples of the second conversation participantcomprises the second conversation participant's verbal expressions orcommunication in a first part of a conversation.

In some embodiments, the detecting the first conversation participant'sfirst conversational activity includes recognizing the firstconversation participant's speech or sound in the stream of digitalsound samples of the first conversation participant. In furtherembodiments, the detecting the second conversation participant's firstconversational activity includes recognizing the second conversationparticipant's speech or sound in the stream of digital sound samples ofthe second conversation participant, in further embodiments, thedetecting the first conversation participant's first conversationalactivity includes recognizing the first conversation participant's faceor body part in the stream of digital pictures of the first conversationparticipant. In further embodiments, the detecting the secondconversation participant's first conversational activity includesrecognizing the second conversation participant's face or body part inthe stream of digital pictures of the second conversation participant.In further embodiments, the detecting the first conversationparticipant's first conversational activity includes determining abeginning and an end of the first conversation participant's firstconversational activity. In further embodiments, the detecting thesecond conversation participant's first conversational activity includesdetermining a beginning and an end of the second conversationparticipant's first conversational activity.

In certain embodiments, the recording of the first conversationparticipant's first conversational activity includes the firstconversation participant's visual expressions or communication in afirst part of a conversation and the first conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the firstconversation participant's first conversational activity includes afirst sub-stream of the stream of digital pictures of the firstconversation participant and a first sub-stream of the stream of digitalsound samples of the first conversation participant. The firstsub-stream of the stream of digital pictures of the first conversationparticipant may comprise the first conversation participant's visualexpressions or communication in a first part of a conversation and thefirst sub-stream of the stream of digital sound samples of the firstconversation participant may comprise the first conversationparticipant's verbal expressions or communication in the first part ofthe conversation. In further embodiments, the recording of the secondconversation participant's first conversational activity includes thesecond conversation participant's visual expressions or communication ina first part of a conversation and the second conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the secondconversation participant's first conversational activity includes afirst sub-stream of the stream of digital pictures of the secondconversation participant and a first sub-stream of the stream of digitalsound samples of the second conversation participant. The firstsub-stream of the stream of digital pictures of the second conversationparticipant may comprise the second conversation participant's visualexpressions or communication in a first part of a conversation and thefirst sub-stream of the stream of digital sound samples of the secondconversation participant may comprise the second conversationparticipant's verbal expressions or communication in the first part ofthe conversation. In further embodiments, the recording of the firstconversation participant's first conversational activity or therecording of the second conversation participant's first conversationalactivity includes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information.

In some embodiments, the first round of conversational exchange includesa unit of knowledge of how the first conversation participant actedrelative to the second conversation participant in a first part of aconversation and how the second conversation participant acted relativeto the first conversation participant in the first part of theconversation. In further embodiments, the first round of conversationalexchange includes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information. In furtherembodiments, the recording of the first conversation participant's firstconversational activity is correlated with the recording of the secondconversation participant's first conversational activity.

In certain embodiments, the stored plurality of rounds of conversationalexchange are organized into at least one of: a neural network, a graph,a collection of sequences, a sequence, a knowledgebase, a knowledgestructure, or a data structure. In further embodiments, each round ofconversational exchange of the stored plurality of rounds ofconversational exchange is included in a neuron, a node, a vertex, or anelement of a neural network, a graph, a collection of sequences, asequence, a knowledgebase, a knowledge structure, or a data structure.In further embodiments, some rounds of conversational exchange of thestored plurality of rounds of conversational exchange areinterconnected.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise: comparing the first round ofconversational exchange with the stored plurality of rounds ofconversational exchange, the comparing performed by the one or moreprocessor circuits of the server, and determining that the storedplurality of rounds of conversational exchange do not include a round ofconversational exchange whose similarity with the first round ofconversational exchange exceeds a similarity threshold, the determiningperformed by the one or more processor circuits of the server.

In some embodiments, the non-transitory computer storage medium and/orthe method further comprise: detecting the first conversationparticipant's second conversational activity from at least one of thestream of digital pictures of the first conversation participant or thestream of digital sound samples of the first conversation participant,the detecting performed by the one or more processor circuits of theserver The non-transitory computer storage medium and/or the methodfurther comprise: detecting the second conversation participant's secondconversational activity from at least one of the stream of digitalpictures of the second conversation participant or the stream of digitalsound samples of the second conversation participant, the detectingperformed by the one or more processor circuits of the server Thenon-transitory computer storage medium and/or the method furthercomprise: generating a second round of conversational exchange includingthe first conversation participant's second conversational activity andthe second conversation participant's second conversational activity,the generating performed by the one or more processor circuits of theserver The non-transitory computer storage medium and/or the methodfurther comprise: storing the second round of conversational exchangeinto the memory unit, the second round of conversational exchange beingpart of the stored plurality of rounds of conversational exchange, thestoring caused by the one or more processor circuits of the server.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise: creating a connection between thestored first round of conversational exchange and the stored secondround of conversational exchange, the creating performed by the one ormore processor circuits of the server. The connection between the storedfirst round of conversational exchange and the stored second round ofconversational exchange may include or be associated with at least oneof: an occurrence count, a weight, a parameter, or a data. In furtherembodiments, the stored plurality of rounds of conversational exchangeare organized into a neural network, and wherein the first round ofconversational exchange is stored into a first node of the neuralnetwork and the second round of conversational exchange is stored into asecond node of the neural network. The first node and the second nodemay be connected by a connection. The first node may be part of a firstlayer of the neural network and the second node may be part of a secondlayer of the neural network. In further embodiments, the storedplurality of rounds of conversational exchange are organized into agraph, and wherein the first round of conversational exchange is storedinto a first node of the graph and the second round of conversationalexchange is stored into a second node of the graph. The first node andthe second node may be connected by a connection. In furtherembodiments, the stored plurality of rounds of conversational exchangeare organized into a collection of sequences, and wherein the firstround of conversational exchange is stored into a first node of asequence of the collection of sequences and the second round ofconversational exchange is stored into a second node of the sequence ofthe collection of sequences. In further embodiments, the storedplurality of rounds of conversational exchange are organized into asequence, and wherein the first round of conversational exchange isstored into a first node of the sequence and the second round ofconversational exchange is stored into a second node of the sequence.The non-transitory computer storage medium and/or the method furthercomprise: comparing the second round of conversational exchange with thestored plurality of rounds of conversational exchange, the comparingperformed by the one or more processor circuits of the server, anddetermining that the stored plurality of rounds of conversationalexchange do not include a round of conversational exchange whosesimilarity with the second round of conversational exchange exceeds asimilarity threshold, the determining performed by the one or moreprocessor circuits of the server. The recording of the firstconversation participant's second conversational activity is correlatedwith the recording of the second conversation participant's secondconversational activity.

In some embodiments, the non-transitory computer storage medium and/orthe method further comprise: updating a connection between the storedfirst round of conversational exchange and another round ofconversational exchange of the stored plurality of rounds ofconversational exchange, the updating performed by the one or moreprocessor circuits of the server.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise: detecting the first conversationparticipant's third conversational activity from at least one of thestream of digital pictures of the first conversation participant or thestream of digital sound samples of the first conversation participant,the detecting performed by the one or more processor circuits of theserver. The non-transitory computer storage medium and/or the methodfurther comprise: detecting the second conversation participant's thirdconversational activity from at least one of the stream of digitalpictures of the second conversation participant or the stream of digitalsound samples of the second conversation participant, the detectingperformed by the one or more processor circuits of the server Thenon-transitory computer storage medium and/or the method furthercomprise: generating a third round of conversational exchange includinga recording of the first conversation participant's third conversationalactivity and a recording of the second conversation participant's thirdconversational activity, the generating performed by the one or moreprocessor circuits of the server The non-transitory computer storagemedium and/or the method further comprise: comparing the third round ofconversational exchange with the stored plurality of rounds ofconversational exchange, the comparing performed by the one or moreprocessor circuits of the server The non-transitory computer storagemedium and/or the method further comprise: determining that the storedplurality of rounds of conversational exchange include a round ofconversational exchange whose similarity with the third round ofconversational exchange exceeds a similarity threshold, the determiningperformed by the one or more processor circuits of the server Thenon-transitory computer storage medium and/or the method furthercomprise: updating a connection between the stored first round ofconversational exchange and the round of conversational exchange whosesimilarity with the third round of conversational exchange exceeds asimilarity threshold, the updating performed by the one or moreprocessor circuits of the server. In further embodiments, the updatingthe connection between the stored first round of conversational exchangeand the round of conversational exchange whose similarity with the thirdround of conversational exchange exceeds a similarity threshold includesupdating at least one of: an occurrence count, a weight, a parameter, ora data included in or associated with the connection. In furtherembodiments, the recording of the first conversation participant's thirdconversational activity is correlated with the recording of the secondconversation participant's third conversational activity.

In some embodiments, the non-transitory computer storage medium and/orthe method further comprise filtering the first conversationparticipant's face or body part from the stream of digital pictures ofthe first conversation participant, the filtering performed by the oneor more processor circuits of the server. In further embodiments, thefiltering the first conversation participant's face or body part fromthe stream of digital pictures of the first conversation participantincludes retaining the first conversation participant's face or bodypart and removing an insignificant content from the stream of digitalpictures of the first conversation participant.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise filtering the first conversationparticipant's speech or sound from the stream of digital sound samplesof the first conversation participant, the filtering performed by theone or more processor circuits of the server. In further embodiments,the filtering the first conversation participant's speech or sound fromthe stream of digital sound samples of the first conversationparticipant includes retaining the first conversation participant'sspeech or sound and removing an insignificant sound from the stream ofdigital sound samples of the first conversation participant.

In some aspects, the disclosure relates to a system for learningartificially intelligent interactive memories. The system may beimplemented at least in part on one or more computing devices. In someembodiments, the system comprises one or more processor circuits coupledto a memory unit The system may further include a firstpicture-capturing device configured to capture a stream of digitalpictures of a first conversation participant and a secondpicture-capturing device configured to capture a stream of digitalpictures of a second conversation participant, the first and the secondpicture-capturing devices coupled to the one or more processor circuitsThe system may further include a first sound-capturing device configuredto capture a stream of digital sound samples of the first conversationparticipant and a second sound-capturing device configured to capture astream of digital sound samples of the second conversation participant,the first and the second sound-capturing devices coupled to the one ormore processor circuits. The one or more processor circuits may beconfigured to: detect the first conversation participant's firstconversational activity from at least one of the stream of digitalpictures of the first conversation participant or the stream of digitalsound samples of the first conversation participant. The one or moreprocessor circuits may be further configured to: detect the secondconversation participant's first conversational activity from at leastone of the stream of digital pictures of the second conversationparticipant or the stream of digital sound samples of the secondconversation participant. The one or more processor circuits may befurther configured to: generate a first round of conversational exchangeincluding a recording of the first conversation participant's firstconversational activity and a recording of the second conversationparticipant's first conversational activity. The one or more processorcircuits may be further configured to: cause the memory unit to storethe first round of conversational exchange, the first round ofconversational exchange being part of a stored plurality of rounds ofconversational exchange.

In certain embodiments, the one or more processor circuits, the memoryunit, the first picture-capturing device, the second picture-capturingdevice, the first sound-capturing device, and the second sound-capturingdevice of the system are included in a single device. In furtherembodiments, at least one of: the one or more processor circuits or thememory unit of the system are included in a server, and wherein thefirst picture-capturing device and the first sound-capturing device ofthe system are included in a first computing device, and the secondpicture-capturing device and the second sound-capturing device of thesystem are included in a second computing device, the first and thesecond computing devices coupled to the server via a network.

In some embodiments, the one or more processor circuits may be furtherconfigured to: compare the first round of conversational exchange withthe stored plurality of rounds of conversational exchange, and determinethat the stored plurality of rounds of conversational exchange do notinclude a round of conversational exchange whose similarity with thefirst round of conversational exchange exceeds a similarity threshold.

In some embodiments, the one or more processor circuits may be furtherconfigured to: detect the first conversation participant's secondconversational activity from at least one of the stream of digitalpictures of the first conversation participant or the stream of digitalsound samples of the first conversation participant. The one or moreprocessor circuits may be further configured to: detect the secondconversation participant's second conversational activity from at leastone of the stream of digital pictures of the second conversationparticipant or the stream of digital sound samples of the secondconversation participant The one or more processor circuits may befurther configured to: generate a second round of conversationalexchange including the first conversation participant's secondconversational activity and the second conversation participant's secondconversational activity The one or more processor circuits may befurther configured to: cause the memory unit to store the second roundof conversational exchange, the second round of conversational exchangebeing part of the stored plurality of rounds of conversational exchange.The one or more processor circuits may be further configured to: createa connection between the stored first round of conversational exchangeand the stored second round of conversational exchange. The one or moreprocessor circuits may be further configured to: compare the secondround of conversational exchange with the stored plurality of rounds ofconversational exchange, and determine that the stored plurality ofrounds of conversational exchange do not include a round ofconversational exchange whose similarity with the second round ofconversational exchange exceeds a similarity threshold.

In certain embodiments, the one or more processor circuits may befurther configured to: detect the first conversation participant's thirdconversational activity from at least one of the stream of digitalpictures of the first conversation participant or the stream of digitalsound samples of the first conversation participant. The one or moreprocessor circuits may be further configured to: detect the secondconversation participant's third conversational activity from at leastone of the stream of digital pictures of the second conversationparticipant or the stream of digital sound samples of the secondconversation participant. The one or more processor circuits may befurther configured to: generate a third round of conversational exchangeincluding a recording of the first conversation participant's thirdconversational activity and a recording of the second conversationparticipant's third conversational activity The one or more processorcircuits may be further configured to: compare the third round ofconversational exchange with the stored plurality of rounds ofconversational exchange. The one or more processor circuits may befurther configured to: determine that the stored plurality of rounds ofconversational exchange include a round of conversational exchange whosesimilarity with the third round of conversational exchange exceeds asimilarity threshold. The one or more processor circuits may be furtherconfigured to: update a connection between the stored first round ofconversational exchange and the round of conversational exchange whosesimilarity with the third round of conversational exchange exceeds asimilarity threshold.

In some embodiments, the one or more processor circuits may be furtherconfigured to: filter the first conversation participant's face or bodypart from the stream of digital pictures of the first conversationparticipant.

In certain embodiments, the one or more processor circuits may befurther configured to: filter the first conversation participant'sspeech or sound from the stream of digital sound samples of the firstconversation participant.

In some aspects, the disclosure relates to a non-transitory computerstorage medium having a computer program stored thereon, the programcomprising instructions that when executed by one or more computingdevices cause the one or more computing devices to perform operationscomprising: capturing a stream of digital pictures of a firstconversation participant by a first picture-capturing device. Theoperations may further include capturing a stream of digital soundsamples of the first conversation participant by a first sound-capturingdevice. The operations may further include capturing a stream of digitalpictures of a second conversation participant by a secondpicture-capturing device. The operations may further include capturing astream of digital sound samples of the second conversation participantby a second sound-capturing device. The operations may further includedetecting the first conversation participant's first conversationalactivity from at least one of the stream of digital pictures of thefirst conversation participant or the stream of digital sound samples ofthe first conversation participant. The operations may further includedetecting the second conversation participant's first conversationalactivity from at least one of the stream of digital pictures of thesecond conversation participant or the stream of digital sound samplesof the second conversation participant. The operations may furtherinclude generating a first round of conversational exchange including arecording of the first conversation participant's first conversationalactivity and a recording of the second conversation participant's firstconversational activity. The operations may further include storing thefirst round of conversational exchange into a memory unit, the firstround of conversational exchange being part of a stored plurality ofrounds of conversational exchange.

In some aspects, the disclosure relates to a method comprising: (a)capturing a stream of digital pictures of a first conversationparticipant by a first picture-capturing device. The method may furtherinclude (b) capturing a stream of digital sound samples of the firstconversation participant by a first sound-capturing device. The methodmay further include (c) capturing a stream of digital pictures of asecond conversation participant by a second picture-capturing device.The method may further include (d) capturing a stream of digital soundsamples of the second conversation participant by a secondsound-capturing device. The method may further include (e) detecting thefirst conversation participant's first conversational activity from atleast one of the stream of digital pictures of the first conversationparticipant or the stream of digital sound samples of the firstconversation participant, the detecting of (e) performed by one or moreprocessor circuits. The method may further include (f) detecting thesecond conversation participant's first conversational activity from atleast one of the stream of digital pictures of the second conversationparticipant or the stream of digital sound samples of the secondconversation participant, the detecting of (f) performed by the one ormore processor circuits. The method may further include (g) generating afirst round of conversational exchange including a recording of thefirst conversation participant's first conversational activity and arecording of the second conversation participant's first conversationalactivity, the generating of (g) performed by the one or more processorcircuits. The method may further include (h) storing the first round ofconversational exchange into a memory unit, the first round ofconversational exchange being part of a stored plurality of rounds ofconversational exchange, the storing of (h) caused by the one or moreprocessor circuits.

The operations or steps of the non-transitory computer storage mediumand/or the method may be performed by any of the elements of the abovedescribed system as applicable. The non-transitory computer storagemedium and/or the method may include any of the operations, steps, andembodiments of the above described system as applicable as well as thefollowing embodiments.

In some embodiments, the one or more processor circuits, the memoryunit, the first picture-capturing device, the second picture-capturingdevice, the first sound-capturing device, and the second sound-capturingdevice of the system are included in a single device. In furtherembodiments, at least one of: the one or more processor circuits or thememory unit of the system are included in a server, and wherein thefirst picture-capturing device and the first sound-capturing device ofthe system are included in a first computing device, and the secondpicture-capturing device and the second sound-capturing device of thesystem are included in a second computing device, the first and thesecond computing devices coupled to the server via a network.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise: comparing the first round ofconversational exchange with the stored plurality of rounds ofconversational exchange, the comparing performed by the one or moreprocessor circuits, and determining that the stored plurality of roundsof conversational exchange do not include a round of conversationalexchange whose similarity with the first round of conversationalexchange exceeds a similarity threshold, the determining performed bythe one or more processor circuits.

In some embodiments, the non-transitory computer storage medium and/orthe method further comprise: detecting the first conversationparticipant's second conversational activity from at least one of thestream of digital pictures of the first conversation participant or thestream of digital sound samples of the first conversation participant,the detecting performed by the one or more processor circuits. Thenon-transitory computer storage medium and/or the method furthercomprise: detecting the second conversation participant's secondconversational activity from at least one of the stream of digitalpictures of the second conversation participant or the stream of digitalsound samples of the second conversation participant, the detectingperformed by the one or more processor circuits. The non-transitorycomputer storage medium and/or the method further comprise: generating asecond round of conversational exchange including the first conversationparticipant's second conversational activity and the second conversationparticipant's second conversational activity, the generating performedby the one or more processor circuits. The non-transitory computerstorage medium and/or the method further comprise: storing the secondround of conversational exchange into a memory unit, the second round ofconversational exchange being part of the stored plurality of rounds ofconversational exchange, the storing caused by the one or more processorcircuits. The non-transitory computer storage medium and/or the methodfurther comprise: creating a connection between the stored first roundof conversational exchange and the stored second round of conversationalexchange, the creating performed by the one or more processor circuits.The non-transitory computer storage medium and/or the method furthercomprise: comparing the second round of conversational exchange with thestored plurality of rounds of conversational exchange, the comparingperformed by the one or more processor circuits, and determining thatthe stored plurality of rounds of conversational exchange do not includea round of conversational exchange whose similarity with the secondround of conversational exchange exceeds a similarity threshold, thedetermining performed by the one or more processor circuits.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise: detecting the first conversationparticipant's third conversational activity from at least one of thestream of digital pictures of the first conversation participant or thestream of digital sound samples of the first conversation participant,the detecting performed by the one or more processor circuits. Thenon-transitory computer storage medium and/or the method furthercomprise: detecting the second conversation participant's thirdconversational activity from at least one of the stream of digitalpictures of the second conversation participant or the stream of digitalsound samples of the second conversation participant, the detectingperformed by the one or more processor circuits. The non-transitorycomputer storage medium and/or the method further comprise: generating athird round of conversational exchange including a recording of thefirst conversation participant's third conversational activity and arecording of the second conversation participant's third conversationalactivity, the generating performed by the one or more processorcircuits. The non-transitory computer storage medium and/or the methodfurther comprise: comparing the third round of conversational exchangewith the stored plurality of rounds of conversational exchange, thecomparing performed by the one or more processor circuits. Thenon-transitory computer storage medium and/or the method furthercomprise: determining that the stored plurality of rounds ofconversational exchange include a round of conversational exchange whosesimilarity with the third round of conversational exchange exceeds asimilarity threshold, the determining performed by the one or moreprocessor circuits. The non-transitory computer storage medium and/orthe method further comprise: updating a connection between the storedfirst round of conversational exchange and the round of conversationalexchange whose similarity with the third round of conversationalexchange exceeds a similarity threshold, the updating performed by theone or more processor circuits.

In some embodiments, the non-transitory computer storage medium and/orthe method further comprise: filtering the first conversationparticipant's face or body part from the stream of digital pictures ofthe first conversation participant, the filtering performed by the oneor more processor circuits.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise: filtering the first conversationparticipant's speech or sound from the stream of digital sound samplesof the first conversation participant, the filtering performed by theone or more processor circuits.

In some aspects, the disclosure relates to a system for learningartificially intelligent interactive memories. The system may beimplemented at least in part on one or more computing devices. In someembodiments, the system comprises one or more processor circuits coupledto a memory unit. The system may further include a picture-capturingdevice configured to capture a stream of digital pictures of a first anda second conversation participants, the picture-capturing device coupledto the one or more processor circuits. The system may further include asound-capturing device configured to capture a stream of digital soundsamples of the first and the second conversation participants, thesound-capturing device coupled to the one or more processor circuits.The one or more processor circuits may be configured to: detect thefirst conversation participant's first conversational activity from atleast one of the stream of digital pictures of the first and the secondconversation participants or the stream of digital sound samples of thefirst and the second conversation participants. The one or moreprocessor circuits may be further configured to: detect the secondconversation participant's first conversational activity from at leastone of the stream of digital pictures of the first and the secondconversation participants or the stream of digital sound samples of thefirst and the second conversation participants. The one or moreprocessor circuits may be further configured to: generate a first roundof conversational exchange including a recording of the firstconversation participant's first conversational activity and a recordingof the second conversation participant's first conversational activity.The one or more processor circuits may be further configured to: causethe memory unit to store the first round of conversational exchange, thefirst round of conversational exchange being part of a stored pluralityof rounds of conversational exchange.

In certain embodiments, the one or more processor circuits, the memoryunit, the picture-capturing device, and the sound-capturing device ofthe system are included in a single device. In further embodiments, atleast one of: the one or more processor circuits or the memory unit ofthe system are included in a server, and wherein the picture-capturingdevice and the sound-capturing device of the system are included in acomputing device, the computing device coupled to the server via anetwork.

In some embodiments, the stream of digital pictures of the first and thesecond conversation participants includes the first and the secondconversation participants' visual expressions or communication. Infurther embodiments, the stream of digital sound samples of the firstand the second conversation participants includes the first and thesecond conversation participants' verbal expressions or communication.In further embodiments, the stream of digital pictures of the first andthe second conversation participants and the stream of digital soundsamples of the first and the second conversation participants arecaptured simultaneously.

In certain embodiments, the first conversation participant's firstconversational activity or the second conversation participant's firstconversational activity includes at least one of: a speaking, a silentfacial expression, a silent body movement, a motionless silence, anabsence from the conversation, or a conversational action. In furtherembodiments, the first conversation participant's first conversationalactivity includes a first conversation participant's speaking and thesecond conversation participant's first conversational activity includesa second conversation participant's silent facial expression, a secondconversation participant's silent body movement, a second conversationparticipant's motionless silence, or a second conversation participant'sabsence from the conversation. In further embodiments, the detecting thefirst conversation participant's speaking includes recognizing the firstconversation participant's speech in the stream of digital sound samplesof the first and the second conversation participants. In furtherembodiments, the detecting the first conversation participant's speakingincludes determining a beginning and an end of the first conversationparticipant's speaking. The determining the beginning of the firstconversation participant's speaking may include recognizing the firstconversation participant's speech after a threshold period of silence inthe stream of digital sound samples of the first and the secondconversation participants. The determining the end of the firstconversation participant's speaking may include recognizing a thresholdperiod of silence after the first conversation participant's speech inthe stream of digital sound samples of the first and the secondconversation participants. In further embodiments, the detecting thesecond conversation participant's silent facial expression includesrecognizing the second conversation participant's facial expression inthe stream of digital pictures of the first and the second conversationparticipants and recognizing the second conversation participant'ssilence in the stream of digital sound samples of the first and thesecond conversation participants. In further embodiments, the detectingthe second conversation participant's silent body movement includesrecognizing the second conversation participant's body movement in thestream of digital pictures of the first and the second conversationparticipants and recognizing the second conversation participant'ssilence in the stream of digital sound samples of the first and thesecond conversation participants. In further embodiments, the detectingthe second conversation participant's motionless silence includesrecognizing no motion or a marginal motion of the second conversationparticipant in the stream of digital pictures of the first and thesecond conversation participants and recognizing the second conversationparticipant's silence in the stream of digital sound samples of thefirst and the second conversation participants. The marginal motion ofthe second conversation participant may include a motion of the secondconversation participant that does not exceed a threshold for motion. Infurther embodiments, the detecting the second conversation participant'sabsence from the conversation includes recognizing the secondconversation participant's absence in the stream of digital pictures ofthe first and the second conversation participants. In furtherembodiments, the first conversation participant's first conversationalactivity includes a first conversation participant's silent facialexpression, a first conversation participant's silent body movement, afirst conversation participant's motionless silence, or a firstconversation participant's absence from the conversation and the secondconversation participant's first conversational activity includes asecond conversation participant's speaking. In further embodiments, thefirst conversation participant's first conversational activity includesa first conversation participant's speaking and the second conversationparticipant's first conversational activity includes a secondconversation participant's speaking. In further embodiments, the firstconversation participant's first conversational activity includes afirst conversation participant's silent facial expression, a firstconversation participant's silent body movement, a first conversationparticipant's motionless silence, or a first conversation participant'sabsence from the conversation and the second conversation participant'sfirst conversational activity includes a second conversationparticipant's silent facial expression, a second conversationparticipant's silent body movement, a second conversation participant'smotionless silence, or a second conversation participant's absence fromthe conversation.

In some embodiments, the timing of the first conversation participant'sfirst conversational activity coincides, partially coincides, oroverlaps with the timing of the second conversation participant's firstconversational activity. In further embodiments, the detecting the firstconversation participant's first conversational activity includesrecognizing the first conversation participant's visual and verbalexpressions or communication in a first part of a conversation.

In certain embodiments, the detecting the first conversationparticipant's first conversational activity includes identifying aparallel sub-stream of the stream of digital pictures of the first andthe second conversation participants, wherein the parallel sub-stream ofthe stream of digital pictures of the first and the second conversationparticipants comprises the first conversation participant's visualexpressions or communication in a first part of a conversation. Infurther embodiments, the detecting the first conversation participant'sfirst conversational activity includes identifying a parallel sub-streamof the stream of digital sound samples of the first and the secondconversation participants, wherein the parallel sub-stream of the streamof digital sound samples of the first and the second conversationparticipants comprises the first conversation participant's verbalexpressions or communication in a first part of a conversation.

In some embodiments, the detecting the second conversation participant'sfirst conversational activity includes recognizing the secondconversation participant's visual and verbal expressions orcommunication in a first part of a conversation. In further embodiments,the detecting the second conversation participant's first conversationalactivity includes identifying a parallel sub-stream of the stream ofdigital pictures of the first and the second conversation participants,wherein the parallel sub-stream of the stream of digital pictures of thefirst and the second conversation participants comprises the secondconversation participant's visual expressions or communication in afirst part of a conversation. In further embodiments, the detecting thesecond conversation participant's first conversational activity includesidentifying a parallel sub-stream of the stream of digital sound samplesof the first and the second conversation participants, wherein theparallel sub-stream of the stream of digital sound samples of the firstand the second conversation participants comprises the secondconversation participant's verbal expressions or communication in afirst part of a conversation.

In certain embodiments, the detecting the first conversationparticipant's first conversational activity includes recognizing thefirst conversation participant's speech or sound in the stream ofdigital sound samples of the first and the second conversationparticipants. In further embodiments, the recognizing the firstconversation participant's speech or sound includes utilizing at leastone of: a speaker dependent speech recognition, or a speech or soundsegmentation. In further embodiments, the detecting the secondconversation participant's first conversational activity includesrecognizing the second conversation participant's speech or sound in thestream of digital sound samples of the first and the second conversationparticipants. In further embodiments, the recognizing the secondconversation participant's speech or sound includes utilizing at leastone of: a speaker dependent speech recognition, or a speech or soundsegmentation. In further embodiments, the detecting the firstconversation participant's first conversational activity includesrecognizing the first conversation participant's face or body part inthe stream of digital pictures of the first and the second conversationparticipants. In further embodiments, the recognizing the firstconversation participant's face or body part includes utilizing apicture segmentation. In further embodiments, the detecting the secondconversation participant's first conversational activity includesrecognizing the second conversation participant's face or body part inthe stream of digital pictures of the first and the second conversationparticipants. In further embodiments, the recognizing the secondconversation participant's face or body part includes utilizing apicture segmentation. In further embodiments, the detecting the firstconversation participant's first conversational activity includesdetermining a beginning and an end of the first conversationparticipant's first conversational activity. In further embodiments, thedetecting the second conversation participant's first conversationalactivity includes determining a beginning and an end of the secondconversation participant's first conversational activity.

In some embodiments, the recording of the first conversationparticipant's first conversational activity includes the firstconversation participant's visual expressions or communication in afirst part of a conversation and the first conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the firstconversation participant's first conversational activity includes aparallel sub-stream of the stream of digital pictures of the first andthe second conversation participants and a parallel sub-stream of thestream of digital sound samples of the first and the second conversationparticipants. In further embodiments, the parallel sub-stream of thestream of digital pictures of the first and the second conversationparticipants comprises the first conversation participant's visualexpressions or communication in a first part of a conversation and theparallel sub-stream of the stream of digital sound samples of the firstand the second conversation participants comprises the firstconversation participant's verbal expressions or communication in thefirst part of the conversation.

In certain embodiments, the recording of the second conversationparticipant's first conversational activity includes the secondconversation participant's visual expressions or communication in afirst part of a conversation and the second conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the secondconversation participant's first conversational activity includes aparallel sub-stream of the stream of digital pictures of the first andthe second conversation participants and a parallel sub-stream of thestream of digital sound samples of the first and the second conversationparticipants. In further embodiments, the parallel sub-stream of thestream of digital pictures of the first and the second conversationparticipants comprises the second conversation participant's visualexpressions or communication in a first part of a conversation and theparallel sub-stream of the stream of digital sound samples of the firstand the second conversation participants comprises the secondconversation participant's verbal expressions or communication in thefirst part of the conversation.

In some embodiments, the one or more processor circuits may be furtherconfigured to: compare the first round of conversational exchange withthe stored plurality of rounds of conversational exchange, and determinethat the stored plurality of rounds of conversational exchange do notinclude a round of conversational exchange whose similarity with thefirst round of conversational exchange exceeds a similarity threshold.

In certain embodiments, the one or more processor circuits may befurther configured to: detect the first conversation participant'ssecond conversational activity from at least one of the stream ofdigital pictures of the first and the second conversation participantsor the stream of digital sound samples of the first and the secondconversation participants. The one or more processor circuits may befurther configured to: detect the second conversation participant'ssecond conversational activity from at least one of the stream ofdigital pictures of the first and the second conversation participantsor the stream of digital sound samples of the first and the secondconversation participants. The one or more processor circuits may befurther configured to: generate a second round of conversationalexchange including the first conversation participant's secondconversational activity and the second conversation participant's secondconversational activity. The one or more processor circuits may befurther configured to: cause the memory unit to store the second roundof conversational exchange, the second round of conversational exchangebeing part of the stored plurality of rounds of conversational exchange.The one or more processor circuits may be further configured to: createa connection between the stored first round of conversational exchangeand the stored second round of conversational exchange. The one or moreprocessor circuits may be further configured to: compare the secondround of conversational exchange with the stored plurality of rounds ofconversational exchange, and determine that the stored plurality ofrounds of conversational exchange do not include a round ofconversational exchange whose similarity with the second round ofconversational exchange exceeds a similarity threshold.

In some embodiments, the one or more processor circuits may be furtherconfigured to: detect the first conversation participant's thirdconversational activity from at least one of the stream of digitalpictures of the first and the second conversation participants or thestream of digital sound samples of the first and the second conversationparticipants. The one or more processor circuits may be furtherconfigured to: detect the second conversation participant's thirdconversational activity from at least one of the stream of digitalpictures of the first and the second conversation participants or thestream of digital sound samples of the first and the second conversationparticipants. The one or more processor circuits may be furtherconfigured to: generate a third round of conversational exchangeincluding the first conversation participant's third conversationalactivity and the second conversation participant's third conversationalactivity. The one or more processor circuits may be further configuredto: compare the third round of conversational exchange with the storedplurality of rounds of conversational exchange. The one or moreprocessor circuits may be further configured to: determine that thestored plurality of rounds of conversational exchange include a round ofconversational exchange whose similarity with the third round ofconversational exchange exceeds a similarity threshold. The one or moreprocessor circuits may be further configured to: update a connectionbetween the stored first round of conversational exchange and the roundof conversational exchange whose similarity with the third round ofconversational exchange exceeds a similarity threshold.

In certain embodiments, the one or more processor circuits may befurther configured to: filter the first and the second conversationparticipants' faces or body parts from the stream of digital pictures ofthe first and the second conversation participants. The one or moreprocessor circuits may be further configured to: the filtering the firstand the second conversation participants' faces or body parts from thestream of digital pictures of the first and the second conversationparticipants includes retaining the first and the second conversationparticipants' faces or body parts and removing an insignificant contentfrom the stream of digital pictures of the first and the secondconversation participants.

In some embodiments, the one or more processor circuits may be furtherconfigured to: filter the first and the second conversationparticipants' speeches or sounds from the stream of digital soundsamples of the first and the second conversation participants. The oneor more processor circuits may be further configured to: the filteringthe first and the second conversation participants' speeches or soundsfrom the stream of digital sound samples of the first and the secondconversation participants includes retaining the first and the secondconversation participants' speeches or sounds and removing aninsignificant sound from the stream of digital sound samples of thefirst and the second conversation participants.

In some aspects, the disclosure relates to a non-transitory computerstorage medium having a computer program stored thereon, the programcomprising instructions that when executed by one or more computingdevices cause the one or more computing devices to perform operationscomprising: capturing a stream of digital pictures of a first and asecond conversation participants by a picture-capturing device. Theoperations may further include capturing a stream of digital soundsamples of the first and the second conversation participants by asound-capturing device. The operations may further include detecting thefirst conversation participant's first conversational activity from atleast one of the stream of digital pictures of the first and the secondconversation participants or the stream of digital sound samples of thefirst and the second conversation participants. The operations mayfurther include detecting the second conversation participant's firstconversational activity from at least one of the stream of digitalpictures of the first and the second conversation participants or thestream of digital sound samples of the first and the second conversationparticipants. The operations may further include generating a firstround of conversational exchange including a recording of the firstconversation participant's first conversational activity and a recordingof the second conversation participant's first conversational activity.The operations may further include storing the first round ofconversational exchange into a memory unit, the first round ofconversational exchange being part of a stored plurality of rounds ofconversational exchange.

In some aspects, the disclosure relates to a method comprising: (a)capturing a stream of digital pictures of a first and a secondconversation participants by a picture-capturing device. The method mayfurther include (b) capturing a stream of digital sound samples of thefirst and the second conversation participants by a sound-capturingdevice. The method may further include (c) detecting the firstconversation participant's first conversational activity from at leastone of the stream of digital pictures of the first and the secondconversation participants or the stream of digital sound samples of thefirst and the second conversation participants, the detecting of (c)performed by one or more processor circuits. The method may furtherinclude (d) detecting the second conversation participant's firstconversational activity from at least one of the stream of digitalpictures of the first and the second conversation participants or thestream of digital sound samples of the first and the second conversationparticipants, the detecting of (d) performed by the one or moreprocessor circuits. The method may further include (e) generating afirst round of conversational exchange including a recording of thefirst conversation participant's first conversational activity and arecording of the second conversation participant's first conversationalactivity, the generating of (e) performed by the one or more processorcircuits. The method may further include (f) storing the first round ofconversational exchange into a memory unit, the first round ofconversational exchange being part of a stored plurality of rounds ofconversational exchange, the storing of (f) caused by the one or moreprocessor circuits.

The operations or steps of the non-transitory computer storage mediumand/or the method may be performed by any of the elements of the abovedescribed system as applicable. The non-transitory computer storagemedium and/or the method may include any of the operations, steps, andembodiments of the above described system as applicable as well as thefollowing embodiments.

In certain embodiments, the one or more processor circuits, the memoryunit, the picture-capturing device, and the sound-capturing device ofthe system are included in a single device. In further embodiments, atleast one of: the one or more processor circuits or the memory unit ofthe system are included in a server, and wherein the picture-capturingdevice and the sound-capturing device of the system are included in acomputing device, the computing device coupled to the server via anetwork.

In some embodiments, the stream of digital pictures of the first and thesecond conversation participants includes the first and the secondconversation participants' visual expressions or communication. Infurther embodiments, the stream of digital sound samples of the firstand the second conversation participants includes the first and thesecond conversation participants' verbal expressions or communication.In further embodiments, the stream of digital pictures of the first andthe second conversation participants and the stream of digital soundsamples of the first and the second conversation participants arecaptured simultaneously.

In certain embodiments, the first conversation participant's firstconversational activity or the second conversation participant's firstconversational activity includes at least one of: a speaking, a silentfacial expression, a silent body movement, a motionless silence, anabsence from the conversation, or a conversational action. In furtherembodiments, the first conversation participant's first conversationalactivity includes a first conversation participant's speaking and thesecond conversation participant's first conversational activity includesa second conversation participant's silent facial expression, a secondconversation participant's silent body movement, a second conversationparticipant's motionless silence, or a second conversation participant'sabsence from the conversation. In further embodiments, the detecting thefirst conversation participant's speaking includes recognizing the firstconversation participant's speech in the stream of digital sound samplesof the first and the second conversation participants. In furtherembodiments, the detecting the first conversation participant's speakingincludes determining a beginning and an end of the first conversationparticipant's speaking. The determining the beginning of the firstconversation participant's speaking may include recognizing the firstconversation participant's speech after a threshold period of silence inthe stream of digital sound samples of the first and the secondconversation participants. The determining the end of the firstconversation participant's speaking may include recognizing a thresholdperiod of silence after the first conversation participant's speech inthe stream of digital sound samples of the first and the secondconversation participants. In further embodiments, the detecting thesecond conversation participant's silent facial expression includesrecognizing the second conversation participant's facial expression inthe stream of digital pictures of the first and the second conversationparticipants and recognizing the second conversation participant'ssilence in the stream of digital sound samples of the first and thesecond conversation participants. In further embodiments, the detectingthe second conversation participant's silent body movement includesrecognizing the second conversation participant's body movement in thestream of digital pictures of the first and the second conversationparticipants and recognizing the second conversation participant'ssilence in the stream of digital sound samples of the first and thesecond conversation participants. In further embodiments, the detectingthe second conversation participant's motionless silence includesrecognizing no motion or a marginal motion of the second conversationparticipant in the stream of digital pictures of the first and thesecond conversation participants and recognizing the second conversationparticipant's silence in the stream of digital sound samples of thefirst and the second conversation participants. The marginal motion ofthe second conversation participant may include a motion of the secondconversation participant that does not exceed a threshold for motion. Infurther embodiments, the detecting the second conversation participant'sabsence from the conversation includes recognizing the secondconversation participant's absence in the stream of digital pictures ofthe first and the second conversation participants. In furtherembodiments, the first conversation participant's first conversationalactivity includes a first conversation participant's silent facialexpression, a first conversation participant's silent body movement, afirst conversation participant's motionless silence, or a firstconversation participant's absence from the conversation and the secondconversation participant's first conversational activity includes asecond conversation participant's speaking. In further embodiments, thefirst conversation participant's first conversational activity includesa first conversation participant's speaking and the second conversationparticipant's first conversational activity includes a secondconversation participant's speaking. In further embodiments, the firstconversation participant's first conversational activity includes afirst conversation participant's silent facial expression, a firstconversation participant's silent body movement, a first conversationparticipant's motionless silence, or a first conversation participant'sabsence from the conversation and the second conversation participant'sfirst conversational activity includes a second conversationparticipant's silent facial expression, a second conversationparticipant's silent body movement, a second conversation participant'smotionless silence, or a second conversation participant's absence fromthe conversation. In further embodiments, the timing of the firstconversation participant's first conversational activity coincides,partially coincides, or overlaps with the timing of the secondconversation participant's first conversational activity.

In certain embodiments, the detecting the first conversationparticipant's first conversational activity includes recognizing thefirst conversation participant's visual and verbal expressions orcommunication in a first part of a conversation. In further embodiments,the detecting the first conversation participant's first conversationalactivity includes identifying a parallel sub-stream of the stream ofdigital pictures of the first and the second conversation participants,wherein the parallel sub-stream of the stream of digital pictures of thefirst and the second conversation participants comprises the firstconversation participant's visual expressions or communication in afirst part of a conversation. In further embodiments, the detecting thefirst conversation participant's first conversational activity includesidentifying a parallel sub-stream of the stream of digital sound samplesof the first and the second conversation participants, wherein theparallel sub-stream of the stream of digital sound samples of the firstand the second conversation participants comprises the firstconversation participant's verbal expressions or communication in afirst part of a conversation.

In some embodiments, the detecting the second conversation participant'sfirst conversational activity includes recognizing the secondconversation participant's visual and verbal expressions orcommunication in a first part of a conversation. In further embodiments,the detecting the second conversation participant's first conversationalactivity includes identifying a parallel sub-stream of the stream ofdigital pictures of the first and the second conversation participants,wherein the parallel sub-stream of the stream of digital pictures of thefirst and the second conversation participants comprises the secondconversation participant's visual expressions or communication in afirst part of a conversation. In further embodiments, the detecting thesecond conversation participant's first conversational activity includesidentifying a parallel sub-stream of the stream of digital sound samplesof the first and the second conversation participants, wherein theparallel sub-stream of the stream of digital sound samples of the firstand the second conversation participants comprises the secondconversation participant's verbal expressions or communication in afirst part of a conversation.

In certain embodiments, the detecting the first conversationparticipant's first conversational activity includes recognizing thefirst conversation participant's speech or sound in the stream ofdigital sound samples of the first and the second conversationparticipants. In further embodiments, wherein the recognizing the firstconversation participant's speech or sound includes utilizing at leastone of: a speaker dependent speech recognition, or a speech or soundsegmentation. In further embodiments, the detecting the secondconversation participant's first conversational activity includesrecognizing the second conversation participant's speech or sound in thestream of digital sound samples of the first and the second conversationparticipants. In further embodiments, the recognizing the secondconversation participant's speech or sound includes utilizing at leastone of: a speaker dependent speech recognition, or a speech or soundsegmentation. In further embodiments, the detecting the firstconversation participant's first conversational activity includesrecognizing the first conversation participant's face or body part inthe stream of digital pictures of the first and the second conversationparticipants. In further embodiments, the recognizing the firstconversation participant's face or body part includes utilizing apicture segmentation. In further embodiments, the detecting the secondconversation participant's first conversational activity includesrecognizing the second conversation participant's face or body part inthe stream of digital pictures of the first and the second conversationparticipants, in further embodiments, the recognizing the secondconversation participant's face or body part includes utilizing apicture segmentation. In further embodiments, the detecting the firstconversation participant's first conversational activity includesdetermining a beginning and an end of the first conversationparticipant's first conversational activity. In further embodiments, thedetecting the second conversation participant's first conversationalactivity includes determining a beginning and an end of the secondconversation participant's first conversational activity.

In some embodiments, the recording of the first conversationparticipant's first conversational activity includes the firstconversation participant's visual expressions or communication in afirst part of a conversation and the first conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the firstconversation participant's first conversational activity includes aparallel sub-stream of the stream of digital pictures of the first andthe second conversation participants and a parallel sub-stream of thestream of digital sound samples of the first and the second conversationparticipants. In further embodiments, the parallel sub-stream of thestream of digital pictures of the first and the second conversationparticipants comprises the first conversation participant's visualexpressions or communication in a first part of a conversation and theparallel sub-stream of the stream of digital sound samples of the firstand the second conversation participants comprises the firstconversation participant's verbal expressions or communication in thefirst part of the conversation.

In certain embodiments, the recording of the second conversationparticipant's first conversational activity includes the secondconversation participant's visual expressions or communication in afirst part of a conversation and the second conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the secondconversation participant's first conversational activity includes aparallel sub-stream of the stream of digital pictures of the first andthe second conversation participants and a parallel sub-stream of thestream of digital sound samples of the first and the second conversationparticipants. In further embodiments, the parallel sub-stream of thestream of digital pictures of the first and the second conversationparticipants comprises the second conversation participant's visualexpressions or communication in a first part of a conversation and theparallel sub-stream of the stream of digital sound samples of the firstand the second conversation participants comprises the secondconversation participant's verbal expressions or communication in thefirst part of the conversation.

In some embodiments, the non-transitory computer storage medium and/orthe method further comprise: comparing the first round of conversationalexchange with the stored plurality of rounds of conversational exchange,the comparing performed by the one or more processor circuits, anddetermining that the stored plurality of rounds of conversationalexchange do not include a round of conversational exchange whosesimilarity with the first round of conversational exchange exceeds asimilarity threshold, the determining performed by the one or moreprocessor circuits.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise: detecting the first conversationparticipant's second conversational activity from at least one of thestream of digital pictures of the first and the second conversationparticipants or the stream of digital sound samples of the first and thesecond conversation participants, the detecting performed by the one ormore processor circuits. The non-transitory computer storage mediumand/or the method further comprise: detecting the second conversationparticipant's second conversational activity from at least one of thestream of digital pictures of the first and the second conversationparticipants or the stream of digital sound samples of the first and thesecond conversation participants, the detecting performed by the one ormore processor circuits. The non-transitory computer storage mediumand/or the method further comprise: generating a second round ofconversational exchange including the first conversation participant'ssecond conversational activity and the second conversation participant'ssecond conversational activity, the generating performed by the one ormore processor circuits. The non-transitory computer storage mediumand/or the method further comprise: storing the second round ofconversational exchange into a memory unit, the second round ofconversational exchange being part of the stored plurality of rounds ofconversational exchange, the storing caused by the one or more processorcircuits. The non-transitory computer storage medium and/or the methodfurther comprise: creating a connection between the stored first roundof conversational exchange and the stored second round of conversationalexchange, the creating performed by the one or more processor circuits.The non-transitory computer storage medium and/or the method furthercomprise: comparing the second round of conversational exchange with thestored plurality of rounds of conversational exchange, the comparingperformed by the one or more processor circuits, and determining thatthe stored plurality of rounds of conversational exchange do not includea round of conversational exchange whose similarity with the secondround of conversational exchange exceeds a similarity threshold, thedetermining performed by the one or more processor circuits.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise: detecting the first conversationparticipant's third conversational activity from at least one of thestream of digital pictures of the first and the second conversationparticipants or the stream of digital sound samples of the first and thesecond conversation participants, the detecting performed by the one ormore processor circuits. The non-transitory computer storage mediumand/or the method further comprise: detecting the second conversationparticipant's third conversational activity from at least one of thestream of digital pictures of the first and the second conversationparticipants or the stream of digital sound samples of the first and thesecond conversation participants, the detecting performed by the one ormore processor circuits. The non-transitory computer storage mediumand/or the method further comprise: generating a third round ofconversational exchange including the first conversation participant'sthird conversational activity and the second conversation participant'sthird conversational activity, the generating performed by the one ormore processor circuits. The non-transitory computer storage mediumand/or the method further comprise: comparing the third round ofconversational exchange with the stored plurality of rounds ofconversational exchange, the comparing performed by the one or moreprocessor circuits. The non-transitory computer storage medium and/orthe method further comprise: determining that the stored plurality ofrounds of conversational exchange include a round of conversationalexchange whose similarity with the third round of conversationalexchange exceeds a similarity threshold, the determining performed bythe one or more processor circuits. The non-transitory computer storagemedium and/or the method further comprise: updating a connection betweenthe stored first round of conversational exchange and the round ofconversational exchange whose similarity with the third round ofconversational exchange exceeds a similarity threshold, the updatingperformed by the one or more processor circuits.

In some embodiments, the non-transitory computer storage medium and/orthe method further comprise: filtering the first and the secondconversation participants' faces or body parts from the stream ofdigital pictures of the first and the second conversation participants,the filtering performed by the one or more processor circuits, infurther embodiments, the filtering the first and the second conversationparticipants' faces or body parts from the stream of digital pictures ofthe first and the second conversation participants includes retainingthe first and the second conversation participants' faces or body partsand removing an insignificant content from the stream of digitalpictures of the first and the second conversation participants.

In certain embodiments, the non-transitory computer storage mediumand/or the method further comprise: filtering the first and the secondconversation participants' speeches or sounds from the stream of digitalsound samples of the first and the second conversation participants, thefiltering performed by the one or more processor circuits. In furtherembodiments, the filtering the first and the second conversationparticipants' speeches or sounds from the stream of digital soundsamples of the first and the second conversation participants includesretaining the first and the second conversation participants' speechesor sounds and removing an insignificant sound from the stream of digitalsound samples of the first and the second conversation participants.

In some aspects, the disclosure relates to a system for learningartificially intelligent interactive memories. The system may beimplemented at least in part on one or more computing devices. In someembodiments, the system comprises one or more processor circuits coupledto a memory unit. The system may further include a firstpicture-capturing device configured to capture a stream of digitalpictures of a first conversation participant and a secondpicture-capturing device configured to capture a stream of digitalpictures of a second conversation participant, the first and the secondpicture-capturing devices coupled to the one or more processor circuits.The system may further include a first sound-capturing device configuredto capture a stream of digital sound samples of the first conversationparticipant and a second sound-capturing device configured to capture astream of digital sound samples of the second conversation participant,the first and the second sound-capturing devices coupled to the one ormore processor circuits. The one or more processor circuits may beconfigured to: detect the first conversation participant's first andsecond conversational activities from at least one of the stream ofdigital pictures of the first conversation participant or the stream ofdigital sound samples of the first conversation participant. The one ormore processor circuits may be further configured to: detect the secondconversation participant's first and second conversational activitiesfrom at least one of the stream of digital pictures of the secondconversation participant or the stream of digital sound samples of thesecond conversation participant. The one or more processor circuits maybe further configured to: generate a first round of conversationalexchange including recordings of the first conversation participant'sfirst and second conversational activities and recordings of the secondconversation participant's first and second conversational activities.The one or more processor circuits may be further configured to: causethe memory unit to store the first round of conversational exchange, thefirst round of conversational exchange being part of a stored pluralityof rounds of conversational exchange.

In some aspects, the disclosure relates to a non-transitory computerstorage medium having a computer program stored thereon, the programcomprising instructions that when executed by one or more computingdevices cause the one or more computing devices to perform operationscomprising: capturing a stream of digital pictures of a firstconversation participant by a first picture-capturing device. Theoperations may further include capturing a stream of digital soundsamples of the first conversation participant by a first sound-capturingdevice. The operations may further include capturing a stream of digitalpictures of a second conversation participant by a secondpicture-capturing device. The operations may further include capturing astream of digital sound samples of the second conversation participantby a second sound-capturing device. The operations may further includedetecting the first conversation participant's first and secondconversational activities from at least one of the stream of digitalpictures of the first conversation participant or the stream of digitalsound samples of the first conversation participant. The operations mayfurther include detecting the second conversation participant's firstand second conversational activities from at least one of the stream ofdigital pictures of the second conversation participant or the stream ofdigital sound samples of the second conversation participant. Theoperations may further include generating a first round ofconversational exchange including recordings of the first conversationparticipant's first and second conversational activities and recordingsof the second conversation participant's first and second conversationalactivities. The operations may further include storing the first roundof conversational exchange into a memory unit, the first round ofconversational exchange being part of a stored plurality of rounds ofconversational exchange.

In some aspects, the disclosure relates to a method comprising: (a)capturing a stream of digital pictures of a first conversationparticipant by a first picture-capturing device. The method may furtherinclude (b) capturing a stream of digital sound samples of the firstconversation participant by a first sound-capturing device. The methodmay further include (c) capturing a stream of digital pictures of asecond conversation participant by a second picture-capturing device.The method may further include (d) capturing a stream of digital soundsamples of the second conversation participant by a secondsound-capturing device. The method may further include (e) detecting thefirst conversation participant's first and second conversationalactivities from at least one of the stream of digital pictures of thefirst conversation participant or the stream of digital sound samples ofthe first conversation participant, the detecting of (e) performed byone or more processor circuits. The method may further include (f)detecting the second conversation participant's first and secondconversational activities from at least one of the stream of digitalpictures of the second conversation participant or the stream of digitalsound samples of the second conversation participant, the detecting of(f performed by the one or more processor circuits. The method mayfurther include (g) generating a first round of conversational exchangeincluding recordings of the first conversation participant's first andsecond conversational activities and recordings of the secondconversation participant's first and second conversational activity, thegenerating of (g) performed by the one or more processor circuits. Themethod may further include (h) storing the first round of conversationalexchange into a memory unit, the first round of conversational exchangebeing part of a stored plurality of rounds of conversational exchange,the storing of (h) caused by the one or more processor circuits.

The operations or steps of the non-transitory computer storage mediumand/or the method may be performed by any of the elements of the abovedescribed system as applicable. The non-transitory computer storagemedium and/or the method may include any of the operations, steps, andembodiments of the above described system as applicable.

In some aspects, the disclosure relates to a system for usingartificially intelligent interactive memories. The system may beimplemented at least in part on one or more computing devices. In someembodiments, the system comprises a server including one or moreprocessor circuits. The system may further include a memory unit,coupled to the one or more processor circuits, that stores a pluralityof rounds of conversational exchange including a first round ofconversational exchange, the first round of conversational exchangecomprising a recording of a first conversation participant's firstconversational activity and a recording of a second conversationparticipant's first conversational activity. The system may furtherinclude a user's computing device including a picture-capturing deviceconfigured to capture a stream of digital pictures of the user andinclude a sound-capturing device configured to capture a stream ofdigital sound samples of the user, the user's computing device coupledto the server via a network. The one or more processor circuits may beconfigured to: detect the user's first conversational activity from atleast one of the stream of digital pictures of the user or the stream ofdigital sound samples of the user. The one or more processor circuitsmay be further configured to: compare at least one portion of arecording of the user's first conversational activity with at least oneportion of the recording of the first conversation participant's firstconversational activity. The one or more processor circuits may befurther configured to: determine that a similarity between at least oneportion of the recording of the user's first conversational activity andat least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold. The one or more processor circuits may be further configuredto: cause a display and a sound-producing device of the user's computingdevice to play at least one portion of the recording of the secondconversation participant's first conversational activity.

In certain embodiments, the recording of the first conversationparticipant's first conversational activity includes the firstconversation participant's visual expressions or communication in afirst part of a conversation and the first conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the firstconversation participant's first conversational activity includes afirst sub-stream of a stream of digital pictures of the firstconversation participant and a first sub-stream of a stream of digitalsound samples of the first conversation participant. In furtherembodiments, the first sub-stream of the stream of digital pictures ofthe first conversation participant comprises the first conversationparticipant's visual expressions or communication in a first part of aconversation and the first sub-stream of the stream of digital soundsamples of the first conversation participant comprises the firstconversation participant's verbal expressions or communication in thefirst part of the conversation, in further embodiments, the recording ofthe second conversation participant's first conversational activityincludes the second conversation participant's visual expressions orcommunication in a first part of a conversation and the secondconversation participant's verbal expressions or communication in thefirst part of the conversation. In further embodiments, the recording ofthe second conversation participant's first conversational activityincludes a first sub-stream of a stream of digital pictures of thesecond conversation participant and a first sub-stream of a stream ofdigital sound samples of the second conversation participant. In furtherembodiments, the first sub-stream of the stream of digital pictures ofthe second conversation participant comprises the second conversationparticipant's visual expressions or communication in a first part of aconversation and the first sub-stream of the stream of digital soundsamples of the second conversation participant comprises the secondconversation participant's verbal expressions or communication in thefirst part of the conversation, in further embodiments, the recording ofthe first conversation participant's first conversational activity orthe recording of the second conversation participant's firstconversational activity includes or is associated with an extrainformation, the extra information comprising a time information, alocation information, an observed information, or a contextualinformation.

In some embodiments, the first round of conversational exchange includesa unit of knowledge of how the first conversation participant actedrelative to the second conversation participant in a first part of aconversation and how the second conversation participant acted relativeto the first conversation participant in the first part of theconversation. In further embodiments, the first round of conversationalexchange includes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information. In furtherembodiments, the recording of the first conversation participant's firstconversational activity is correlated with the recording of the secondconversation participant's first conversational activity.

In certain embodiments, the stored plurality of rounds of conversationalexchange are organized into at least one of: a neural network, a graph,a collection of sequences, a sequence, a knowledgebase, a knowledgestructure, or a data structure. In further embodiments, each round ofconversational exchange of the stored plurality of rounds ofconversational exchange is included in a neuron, a node, a vertex, or anelement of a neural network, a graph, a collection of sequences, asequence, a knowledgebase, a knowledge structure, or a data structure.In further embodiments, some rounds of conversational exchange of thestored plurality of rounds of conversational exchange areinterconnected.

In some embodiments, the picture-capturing device includes a motionpicture camera. In further embodiments, the stream of digital picturesof the user includes the user's visual expressions or communication. Infurther embodiments, the stream of digital pictures of the user includesa digital motion picture. The digital motion picture may include a MPEGmotion picture, an AVI motion picture, a FLV motion picture, a MOVmotion picture, a RM motion picture, a SWF motion picture, a WMV motionpicture, a DivX motion picture, or a digitally encoded motion picture.In further embodiments, the stream of digital pictures of the userincludes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information.

In certain embodiments, the sound-capturing device includes amicrophone. In further embodiments, the stream of digital sound samplesof the user includes the user's verbal expressions or communication. Infurther embodiments, the stream of digital sound samples of the userincludes a digital sound. The digital sound may include a WAV digitalsound, a WMA digital sound, an AIFF digital sound, a MP3 digital sound,a RA digital sound, a OGG digital sound, or a digitally encoded sound.In further embodiments, the stream of digital sound samples of the userincludes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information. In furtherembodiments, the stream of digital pictures of the user and the streamof digital sound samples of the user are captured simultaneously.

In some embodiments, the user's first conversational activity includesat least one of: a user's speaking, a user's silent facial expression, auser's silent body movement, a user's motionless silence, a user'sabsence from the conversation, or a user's conversational action. Infurther embodiments, the detecting the user's speaking includesrecognizing the users speech in the stream of digital sound samples ofthe user. In further embodiments, the detecting the user's speakingincludes determining a beginning and an end of the user's speaking. Thedetermining the beginning of the user's speaking may include recognizingthe user's speech after a threshold period of silence in the stream ofdigital sound samples of the user. The determining the end of the user'sspeaking may include recognizing a threshold period of silence after theuser's speech in the stream of digital sound samples of the user. Infurther embodiments, the detecting the user's silent facial expressionincludes recognizing the user's facial expression in the stream ofdigital pictures of the user and recognizing the user's silence in thestream of digital sound samples of the user. In further embodiments, thedetecting the user's silent body movement includes recognizing theuser's body movement in the stream of digital pictures of the user andrecognizing the user's silence in the stream of digital sound samples ofthe user. In further embodiments, the detecting the user's motionlesssilence includes recognizing no motion or a marginal motion of the userin the stream of digital pictures of the user and recognizing the user'ssilence in the stream of digital sound samples of the user. The marginalmotion of the user may include a motion of the user that does not exceeda threshold for motion. In further embodiments, the detecting the user'sabsence from the conversation includes recognizing the user's absence inthe stream of digital pictures of the user. In further embodiments, thedetecting the user's first conversational activity includes recognizingthe user's visual and verbal expressions or communication in a firstpart of a simulated conversation. In further embodiments, the detectingthe user's first conversational activity includes identifying a firstsub-stream of the stream of digital pictures of the user, wherein thefirst sub-stream of the stream of digital pictures of the user comprisesthe user's visual expressions or communication in a first part of asimulated conversation. In further embodiments, the detecting the user'sfirst conversational activity includes identifying a first sub-stream ofthe stream of digital sound samples of the user, wherein the firstsub-stream of the stream of digital sound samples of the user comprisesthe user's verbal expressions or communication in a first part of asimulated conversation.

In certain embodiments, the detecting the user's first conversationalactivity includes recognizing the user's speech or sound in the streamof digital sound samples of the user. In further embodiments, thedetecting the user's first conversational activity includes recognizingthe user's face or body part in the stream of digital pictures of theuser. In further embodiments, the detecting the user's firstconversational activity includes determining a beginning and an end ofthe user's first conversational activity.

In some embodiments, the recording of the user's first conversationalactivity includes the user's visual expressions or communication in afirst part of a simulated conversation and the user's verbal expressionsor communication in the first part of the simulated conversation.

In certain embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital soundsamples of the user and the recording of the first conversationparticipant's first conversational activity includes a first sub-streamof a stream of digital sound samples of the first conversationparticipant, wherein the comparing at least one portion of the recordingof the user's first conversational activity with at least one portion ofthe recording of the first conversation participant's firstconversational activity includes comparing at least one portion of thefirst sub-stream of the stream of digital sound samples of the user withat least one portion of the first sub-stream of the stream of digitalsound samples of the first conversation participant. In furtherembodiments, the portion of the first sub-stream of the stream ofdigital sound samples of the user includes one or more words, one ormore features, or one or more sound samples of the first sub-stream ofthe stream of digital sound samples of the user. In further embodiments,the portion of the first sub-stream of the stream of digital soundsamples of the first conversation participant includes one or morewords, one or more features, or one or more sound samples of the firstsub-stream of a stream of digital sound samples of the firstconversation participant.

In some embodiments. In further embodiments, the comparing at least oneportion of the recording of the user's first conversational activitywith at least one portion of the recording of the first conversationparticipant's first conversational activity includes comparing at leastone word recognized from the recording of the user's firstconversational activity with at least one word recognized from therecording of the first conversation participant's first conversationalactivity. In further embodiments, the comparing at least one portion ofthe recording of the user's first conversational activity with at leastone portion of the recording of the first conversation participant'sfirst conversational activity includes comparing at least one soundfeature from the recording of the user's first conversational activitywith at least one sound feature from the recording of the firstconversation participant's first conversational activity. In furtherembodiments, the comparing at least one portion of the recording of theuser's first conversational activity with at least one portion of therecording of the first conversation participant's first conversationalactivity includes comparing at least one sound sample from the recordingof the user's first conversational activity with at least one soundsample from the recording of the first conversation participant's firstconversational activity. In further embodiments, the comparing at leastone portion of the recording of the user's first conversational activitywith at least one portion of the recording of the first conversationparticipant's first conversational activity includes performing at leastone of: amplitude adjustment, sample rate adjustment, noise reduction,or temporal alignment of one or more sound samples in the recording ofthe user's first conversational activity or the recording of the firstconversation participant's first conversational activity.

In some embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital soundsamples of the user and the recording of the first conversationparticipant's first conversational activity includes a first sub-streamof a stream of digital sound samples of the first conversationparticipant, wherein the comparing at least one portion of the recordingof the user's first conversational activity with at least one portion ofthe recording of the first conversation participant's firstconversational activity includes performing at least one of: amplitudeadjustment, sample rate adjustment, noise reduction, or temporalalignment in the first sub-stream of the stream of digital sound samplesof the user or the first sub-stream of the stream of digital soundsamples of the first conversation participant. In further embodiments,the recording of the user's first conversational activity includes afirst sub-stream of the stream of digital pictures of the user and therecording of the first conversation participant's first conversationalactivity includes a first sub-stream of a stream of digital pictures ofthe first conversation participant, wherein the comparing at least oneportion of the recording of the user's first conversational activitywith at least one portion of the recording of the first conversationparticipant's first conversational activity includes comparing at leastone portion of the first sub-stream of the stream of digital pictures ofthe user with at least one portion of the first sub-stream of the streamof digital pictures of the first conversation participant. In furtherembodiments, the portion of the first sub-stream of the stream ofdigital pictures of the user includes one or more pictures, one or moreregions of a picture, one or more features of a picture, or one or morepixels of a picture of the first sub-stream of the stream of digitalpictures of the user. In further embodiments, the portion of the firstsub-stream of the stream of digital pictures of the first conversationparticipant includes one or more pictures, one or more regions of apicture, one or more features of a picture, or one or more pixels of apicture of the first sub-stream of the stream of digital pictures of thefirst conversation participant. In further embodiments, the comparing atleast one portion of the recording of the user's first conversationalactivity with at least one portion of the recording of the firstconversation participant's first conversational activity includescomparing at least one picture from the recording of the user's firstconversational activity with at least one picture from the recording ofthe first conversation participant's first conversational activity. Infurther embodiments, the comparing at least one portion of the recordingof the user's first conversational activity with at least one portion ofthe recording of the first conversation participant's firstconversational activity includes comparing at least one region of apicture from the recording of the user's first conversational activitywith at least one region of a picture from the recording of the firstconversation participant's first conversational activity. In furtherembodiments, the comparing at least one portion of the recording of theuser's first conversational activity with at least one portion of therecording of the first conversation participant's first conversationalactivity includes comparing at least one picture feature from therecording of the user's first conversational activity with at least onepicture feature from the recording of the first conversationparticipant's first conversational activity. In further embodiments, thecomparing at least one portion of a recording of the user's firstconversational activity with at least one portion of the recording ofthe first conversation participant's first conversational activityincludes comparing at least one pixel from the recording of the user'sfirst conversational activity with at least one pixel from the recordingof the first conversation participant's first conversational activity.In further embodiments, the comparing at least one portion of therecording of the user's first conversational activity with at least oneportion of the recording of the first conversation participant's firstconversational activity includes performing at least one of: coloradjustment, size adjustment, transparency utilization, mask utilization,or temporal alignment of one or more pictures in the recording of theuser's first conversational activity or the recording of the firstconversation participant's first conversational activity.

In certain embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital picturesof the user and the recording of the first conversation participant'sfirst conversational activity includes a first sub-stream of a stream ofdigital pictures of the first conversation participant, wherein thecomparing at least one portion of the recording of the user's firstconversational activity with at least one portion of the recording ofthe first conversation participant's first conversational activityincludes performing at least one of: color adjustment, size adjustment,transparency utilization, mask utilization, or temporal alignment in thefirst sub-stream of the stream of digital pictures of the user or thefirst sub-stream of the stream of digital pictures of the firstconversation participant.

In certain embodiments, the comparing at least one portion of therecording of the user's first conversational activity with at least oneportion of the recording of the first conversation participant's firstconversational activity includes comparing an extra information includedin the recording of the user's first conversational activity with anextra information included in the recording of the first conversationparticipant's first conversational activity. In further embodiments, theextra information includes a time information, a location information,an observed information, or a contextual information.

In some embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a similarity between an extrainformation included in the recording of the user's first conversationalactivity and an extra information included in the recording of the firstconversation participant's first conversational activity exceeds asimilarity threshold. In further embodiments, the extra informationincludes a time information, a location information, an observedinformation, or a contextual information.

In certain embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital soundsamples of the user and the recording of the first conversationparticipant's first conversational activity includes a first sub-streamof a stream of digital sound samples of the first conversationparticipant, wherein the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a similarity between at least oneportion of the first sub-stream of the stream of digital sound samplesof the user and at least one portion of the first sub-stream of thestream of digital sound samples of the first conversation participantexceeds a similarity threshold. In further embodiments, the portion ofthe first sub-stream of the stream of digital sound samples of the userincludes one or more words, one or more features, or one or more soundsamples of the first sub-stream of the stream of digital sound samplesof the user. In further embodiments, the portion of the first sub-streamof the stream of digital sound samples of the first conversationparticipant includes one or more words, one or more features, or one ormore sound samples of the first sub-stream of the stream of digitalsound samples of the first conversation participant.

In some embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingwords recognized from the recording of the user's first conversationalactivity and from the recording of the first conversation participant'sfirst conversational activity exceeds a threshold. In furtherembodiments, the matching words recognized from the recording of theuser's first conversational activity and from the recording of the firstconversation participant's first conversational activity are matchedfactoring in at least one of: an order of a word, a type of a word, animportance of a word, a semantic variation of a word, a concept of aword, or a threshold for a difference in a word. In further embodiments,the determining that a similarity between at least one portion of therecording of the user's first conversational activity and at least oneportion of the recording of the first conversation participant's firstconversational activity exceeds a similarity threshold includesdetermining that a number or a percentage of matching sound featuresfrom the recording of the user's first conversational activity and fromthe recording of the first conversation participant's firstconversational activity exceeds a threshold. In further embodiments, thematching sound features from the recording of the user's firstconversational activity and from the recording of the first conversationparticipant's first conversational activity are matched factoring in atleast one of: an order of a sound feature, a type of a sound feature, animportance of a sound feature, or a threshold for a difference in asound feature. In further embodiments, the determining that a similaritybetween at least one portion of the recording of the user's firstconversational activity and at least one portion of the recording of thefirst conversation participant's first conversational activity exceeds asimilarity threshold includes determining that a number or a percentageof matching sound samples from the recording of the user's firstconversational activity and from the recording of the first conversationparticipant's first conversational activity exceeds a threshold. Infurther embodiments, the matching sound samples from the recording ofthe user's first conversational activity and from the recording of thefirst conversation participant's first conversational activity arematched factoring in at least one of: an order of a sound sample, animportance of a sound sample, or a threshold for a difference in a soundsample. In further embodiments, the determining that a similaritybetween at least one portion of the recording of the user's firstconversational activity and at least one portion of the recording of thefirst conversation participant's first conversational activity exceeds asimilarity threshold includes recognizing a speech or a sound of a sameperson in the at least one portion of the recording of the user's firstconversational activity and the at least one portion of the recording ofthe first conversation participant's first conversational activity.

In certain embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital picturesof the user and the recording of the first conversation participant'sfirst conversational activity includes a first sub-stream of a stream ofdigital pictures of the first conversation participant, and

wherein the determining that a similarity between at least one portionof the recording of the user's first conversational activity and atleast one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a similarity between at least oneportion of the first sub-stream of the stream of digital pictures of theuser and at least one portion of the first sub-stream of the stream ofdigital pictures of the first conversation participant exceeds asimilarity threshold. In further embodiments, the portion of the firstsub-stream of the stream of digital pictures of the user includes one ormore pictures, one or more regions of a picture, one or more features ofa picture, or one or more pixels of a picture of the first sub-stream ofthe stream of digital pictures of the user. In further embodiments, theportion of the first sub-stream of the stream of digital pictures of thefirst conversation participant includes one or more pictures, one ormore regions of a picture, one or more features of a picture, or one ormore pixels of a picture of the first sub-stream of a stream of digitalpictures of the first conversation participant.

In some embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingpictures from the recording of the user's first conversational activityand from the recording of the first conversation participant's firstconversational activity exceeds a threshold. In further embodiments, thematching pictures from the recording of the user's first conversationalactivity and from the recording of the first conversation participant'sfirst conversational activity are matched factoring in at least one of:an order of a picture, or a threshold for a difference in a picture. Infurther embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingregions of pictures from the recording of the user's firstconversational activity and from the recording of the first conversationparticipant's first conversational activity exceeds a threshold. Infurther embodiments, the matching regions of a picture from therecording of the user's first conversational activity and from therecording of the first conversation participant's first conversationalactivity are matched factoring in at least one of: a location of aregion, or a threshold for a difference in a region. In furtherembodiments, the determining that a similarity between at least oneportion of the recording of the user's first conversational activity andat least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingpicture features from the recording of the user's first conversationalactivity and from the recording of the first conversation participant'sfirst conversational activity exceeds a threshold. In furtherembodiments, the matching picture features from the recording of theuser's first conversational activity and from the recording of the firstconversation participant's first conversational activity are matchedfactoring in at least one of: a type of a picture feature, an importanceof a picture feature, a location of a picture feature in a region ofinterest, or a threshold for a difference in a picture feature. Infurther embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingpixels from the recording of the user's first conversational activityand from the recording of the first conversation participant's firstconversational activity exceeds a threshold. In further embodiments, thematching pixels from the recording of the user's first conversationalactivity and from the recording of the first conversation participant'sfirst conversational activity are matched factoring in at least one of:a location of a pixel in a region of interest, or a threshold for adifference in a pixel.

In some embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes recognizing a same person or object in the at leastone portion of the recording of the user's first conversational activityand the at least one portion of the recording of the first conversationparticipant's first conversational activity.

In certain embodiments, the playing the at least one portion of therecording of the second conversation participant's first conversationalactivity is performed concurrently with at least one portion of theuser's first conversational activity. In further embodiments, therecording of the second conversation participant's first conversationalactivity includes a first sub-stream of a stream of digital pictures ofthe second conversation participant, wherein the playing the at leastone portion of the recording of the second conversation participant'sfirst conversational activity includes playing the at least one portionof the first sub-stream of the stream of digital pictures of the secondconversation participant. In further embodiments, the recording of thesecond conversation participant's first conversational activity includesa first sub-stream of a stream of digital sound samples of the secondconversation participant, wherein the playing the at least one portionof the recording of the second conversation participant's firstconversational activity includes playing the at least one portion of thefirst sub-stream of the stream of digital sound samples of the secondconversation participant.

In some embodiments, the stored plurality of rounds of conversationalexchange include a second round of conversational exchange, the secondround of conversational exchange comprising a recording of a firstconversation participant's second conversational activity and arecording of a second conversation participant's second conversationalactivity. The one or more processor circuits may be further configuredto: detect the user's second conversational activity from at least oneof the stream of digital pictures of the user or the stream of digitalsound samples of the user. The one or more processor circuits may befurther configured to: compare at least one portion of a recording ofthe user's second conversational activity with at least one portion ofthe recording of the first conversation participant's secondconversational activity. The one or more processor circuits may befurther configured to: determine that a similarity between at least oneportion of the recording of the user's second conversational activityand at least one portion of the recording of the first conversationparticipant's second conversational activity exceeds a similaritythreshold. The one or more processor circuits may be further configuredto: cause the display and the sound-producing device of the user'scomputing device to play at least one portion of the recording of thesecond conversation participant's second conversational activity. Infurther embodiments, the first round of conversational exchange isconnected to the second round of conversational exchange by aconnection. The connection between the first round of conversationalexchange and the second round of conversational exchange may include orbe associated with at least one of: an occurrence count, a weight, aparameter, or a data. In further embodiments, the stored plurality ofrounds of conversational exchange are organized into a neural network,and wherein the first round of conversational exchange is stored into afirst node of the neural network and the second round of conversationalexchange is stored into a second node of the neural network. The firstnode and the second node may be connected by a connection. The firstnode may be part of a first layer of the neural network and the secondnode may be part of a second layer of the neural network. In furtherembodiments, the stored plurality of rounds of conversational exchangeare organized into a graph, and wherein the first round ofconversational exchange is stored into a first node of the graph and thesecond round of conversational exchange is stored into a second node ofthe graph. The first node and the second node may be connected by aconnection. In further embodiments, the stored plurality of rounds ofconversational exchange are organized into a collection of sequences,and wherein the first round of conversational exchange is stored into afirst node of a sequence of the collection of sequences and the secondround of conversational exchange is stored into a second node of thesequence of the collection of sequences. In further embodiments, thestored plurality of rounds of conversational exchange are organized intoa sequence, and wherein the first round of conversational exchange isstored into a first node of the sequence and the second round ofconversational exchange is stored into a second node of the sequence.

In some embodiments, the playing the at least one portion of therecording of the second conversation participant's second conversationalactivity is performed concurrently with the at least one portion of theuser's second conversational activity. In further embodiments, theplaying the at least one portion of the recording of the secondconversation participant's second conversational activity is performedsubsequent to the at least one portion of the user's firstconversational activity.

In certain embodiments, the playing the at least one portion of therecording of the second conversation participant's second conversationalactivity includes transitioning from the at least one portion of therecording of the second conversation participant's first conversationalactivity to the at least one portion of the recording of the secondconversation participant's second conversational activity. In furtherembodiments, the transitioning from the at least one portion of therecording of the second conversation participant's first conversationalactivity to the at least one portion of the recording of the secondconversation participant's second conversational activity includes atleast one of: moving, centering, aligning, resizing, or transforming oneor more pictures of the recording of the second conversationparticipant's first conversational activity and one or more pictures ofthe recording of the second conversation participant's secondconversational activity. In further embodiments, the transitioning fromthe at least one portion of the recording of the second conversationparticipant's first conversational activity to the at least one portionof the recording of the second conversation participant's secondconversational activity includes adjusting a lighting or a color of oneor more pictures of the recording of the second conversationparticipant's first conversational activity and one or more pictures ofthe recording of the second conversation participant's secondconversational activity. In further embodiments, the transitioning fromthe at least one portion of the recording of the second conversationparticipant's first conversational activity to the at least one portionof the recording of the second conversation participant's secondconversational activity includes a cut or a dissolve between one or morepictures of the recording of the second conversation participant's firstconversational activity and one or more pictures of the recording of thesecond conversation participant's second conversational activity. Infurther embodiments, the transitioning from the at least one portion ofthe recording of the second conversation participant's firstconversational activity to the at least one portion of the recording ofthe second conversation participant's second conversational activityincludes morphing of one or more pictures of the recording of the secondconversation participant's first conversational activity and one or morepictures of the recording of the second conversation participant'ssecond conversational activity.

In some embodiments, the playing the at least one portion of therecording of the second conversation participant's second conversationalactivity includes bridging between the at least one portion of therecording of the second conversation participant's first conversationalactivity and the at least one portion of the recording of the secondconversation participant's second conversational activity. In furtherembodiments, the bridging between the at least one portion of therecording of the second conversation participant's first conversationalactivity and the at least one portion of the recording of the secondconversation participant's second conversational activity includesinterpolation, inbetweening, extrapolation, or picture generationbetween one or more pictures of the recording of the second conversationparticipant's first conversational activity and one or more pictures ofthe recording of the second conversation participant's secondconversational activity. In further embodiments, the bridging betweenthe at least one portion of the recording of the second conversationparticipant's first conversational activity and the at least one portionof the recording of the second conversation participant's secondconversational activity includes playing or replaying one or morepictures of the recording of the second conversation participant's firstconversational activity.

In some aspects, the disclosure relates to a non-transitory computerstorage medium having a computer program stored thereon, the programcomprising instructions that when executed by one or more computingdevices cause the one or more computing devices to perform operationscomprising: accessing a memory unit of a server that stores a pluralityof rounds of conversational exchange including a first round ofconversational exchange, the first round of conversational exchangecomprising a recording of a first conversation participant's firstconversational activity and a recording of a second conversationparticipant's first conversational activity. The operations may furtherinclude capturing a stream of digital pictures of a user by apicture-capturing device of a user's computing device. The operationsmay further include capturing a stream of digital sound samples of theuser by a sound-capturing device of the user's computing device, theuser's computing device coupled to the server via a network. Theoperations may further include detecting the user's first conversationalactivity from at least one of the stream of digital pictures of the useror the stream of digital sound samples of the user. The operations mayfurther include comparing at least one portion of a recording of theuser's first conversational activity with at least one portion of therecording of the first conversation participant's first conversationalactivity. The operations may further include determining that asimilarity between at least one portion of the recording of the user'sfirst conversational activity and at least one portion of the recordingof the first conversation participant's first conversational activityexceeds a similarity threshold. The operations may further includeplaying at least one portion of the recording of the second conversationparticipant's first conversational activity by a display and asound-producing device of the user's computing device.

In some aspects, the disclosure relates to a method comprising: (a)accessing a memory unit of a server that stores a plurality of rounds ofconversational exchange including a first round of conversationalexchange, the first round of conversational exchange comprising arecording of a first conversation participant's first conversationalactivity and a recording of a second conversation participant's firstconversational activity, the accessing of (a) performed by one or moreprocessor circuits of the server. The method may further include (b)capturing a stream of digital pictures of a user by a picture-capturingdevice of a user's computing device. The method may further include (c)capturing a stream of digital sound samples of the user by asound-capturing device of the user's computing device, the user'scomputing device coupled to the server via a network. The method mayfurther include (d) detecting the user's first conversational activityfrom at least one of the stream of digital pictures of the user or thestream of digital sound samples of the user, the detecting of (d)performed by the one or more processor circuits of the server. Themethod may further include (e) comparing at least one portion of arecording of the user's first conversational activity with at least oneportion of the recording of the first conversation participant's firstconversational activity, the comparing of (e) performed by the one ormore processor circuits of the server. The method may further include(f) determining that a similarity between at least one portion of therecording of the user's first conversational activity and at least oneportion of the recording of the first conversation participant's firstconversational activity exceeds a similarity threshold, the determiningof (f) performed by the one or more processor circuits of the server.The method may further include (g) playing at least one portion of therecording of the second conversation participant's first conversationalactivity by a display and a sound-producing device of the user'scomputing device, the playing of (g) caused by the one or more processorcircuits of the server.

The operations or steps of the non-transitory computer storage mediumand/or the method may be performed by any of the elements of the abovedescribed system as applicable. The non-transitory computer storagemedium and/or the method may include any of the operations, steps, andembodiments of the above described system as applicable as well as thefollowing embodiments.

In certain embodiments, the recording of the first conversationparticipant's first conversational activity includes the firstconversation participant's visual expressions or communication in afirst part of a conversation and the first conversation participant'sverbal expressions or communication in the first part of theconversation. In further embodiments, the recording of the firstconversation participant's first conversational activity includes afirst sub-stream of a stream of digital pictures of the firstconversation participant and a first sub-stream of a stream of digitalsound samples of the first conversation participant. In furtherembodiments, the first sub-stream of the stream of digital pictures ofthe first conversation participant comprises the first conversationparticipant's visual expressions or communication in a first part of aconversation and the first sub-stream of the stream of digital soundsamples of the first conversation participant comprises the firstconversation participant's verbal expressions or communication in thefirst part of the conversation. In further embodiments, the recording ofthe second conversation participant's first conversational activityincludes the second conversation participant's visual expressions orcommunication in a first part of a conversation and the secondconversation participant's verbal expressions or communication in thefirst part of the conversation. In further embodiments, the recording ofthe second conversation participant's first conversational activityincludes a first sub-stream of a stream of digital pictures of thesecond conversation participant and a first sub-stream of a stream ofdigital sound samples of the second conversation participant. In furtherembodiments, the first sub-stream of the stream of digital pictures ofthe second conversation participant comprises the second conversationparticipant's visual expressions or communication in a first part of aconversation and the first sub-stream of the stream of digital soundsamples of the second conversation participant comprises the secondconversation participant's verbal expressions or communication in thefirst part of the conversation. In further embodiments, the recording ofthe first conversation participant's first conversational activity orthe recording of the second conversation participant's firstconversational activity includes or is associated with an extrainformation, the extra information comprising a time information, alocation information, an observed information, or a contextualinformation.

In certain embodiments, the first round of conversational exchangeincludes a unit of knowledge of how the first conversation participantacted relative to the second conversation participant in a first part ofa conversation and how the second conversation participant actedrelative to the first conversation participant in the first part of theconversation. In further embodiments, the first round of conversationalexchange includes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information. In furtherembodiments, the recording of the first conversation participant's firstconversational activity is correlated with the recording of the secondconversation participant's first conversational activity.

In some embodiments, the stored plurality of rounds of conversationalexchange are organized into at least one of a neural network, a graph, acollection of sequences, a sequence, a knowledgebase, a knowledgestructure, or a data structure. In further embodiments, each round ofconversational exchange of the stored plurality of rounds ofconversational exchange is included in a neuron, a node, a vertex, or anelement of a neural network, a graph, a collection of sequences, asequence, a knowledgebase, a knowledge structure, or a data structure.In further embodiments, some rounds of conversational exchange of thestored plurality of rounds of conversational exchange areinterconnected.

In certain embodiments, the picture-capturing device includes a motionpicture camera. In further embodiments, the stream of digital picturesof the user includes the user's visual expressions or communication. Infurther embodiments, the stream of digital pictures of the user includesa digital motion picture. The digital motion picture may include a MPEGmotion picture, an AVI motion picture, a FLV motion picture, a MOVmotion picture, a RM motion picture, a SWF motion picture, a WMV motionpicture, a DivX motion picture, or a digitally encoded motion picture,in further embodiments, the stream of digital pictures of the userincludes or is associated with an extra information, the extrainformation comprising a time information, a location information, anobserved information, or a contextual information.

In some embodiments, the sound-capturing device includes a microphone.In further embodiments, the stream of digital sound samples of the userincludes the user's verbal expressions or communication. In furtherembodiments, the stream of digital sound samples of the user includes adigital sound. The digital sound may include a WAV digital sound, a WMAdigital sound, an RIFF digital sound, a MP3 digital sound, a RA digitalsound, a OGG digital sound, or a digitally encoded sound. In furtherembodiments, the stream of digital sound samples of the user includes oris associated with an extra information, the extra informationcomprising a time information, a location information, an observedinformation, or a contextual information. In further embodiments, thestream of digital pictures of the user and the stream of digital soundsamples of the user are captured simultaneously.

In certain embodiments, the user's first conversational activityincludes at least one of: a user's speaking, a user's silent facialexpression, a user's silent body movement, a user's motionless silence,a user's absence from the conversation, or a user's conversationalaction. In further embodiments, the detecting the user's speakingincludes recognizing the user's speech in the stream of digital soundsamples of the user. In further embodiments, the detecting the user'sspeaking includes determining a beginning and an end of the user'sspeaking. The determining the beginning of the user's speaking mayinclude recognizing the user's speech after a threshold period ofsilence in the stream of digital sound samples of the user. Thedetermining the end of the user's speaking includes recognizing athreshold period of silence after the user's speech in the stream ofdigital sound samples of the user. In further embodiments, the detectingthe user's silent facial expression includes recognizing the user'sfacial expression in the stream of digital pictures of the user andrecognizing the user's silence in the stream of digital sound samples ofthe user. In further embodiments, the detecting the user's silent bodymovement includes recognizing the user's body movement in the stream ofdigital pictures of the user and recognizing the user's silence in thestream of digital sound samples of the user. In further embodiments, thedetecting the user's motionless silence includes recognizing no motionor a marginal motion of the user in the stream of digital pictures ofthe user and recognizing the user's silence in the stream of digitalsound samples of the user. The marginal motion of the user includes amotion of the user that does not exceed a threshold for motion. Infurther embodiments, the detecting the user's absence from theconversation includes recognizing the user's absence in the stream ofdigital pictures of the user.

In some embodiments. In further embodiments, the detecting the user'sfirst conversational activity includes recognizing the user's visual andverbal expressions or communication in a first part of a simulatedconversation. In further embodiments, the detecting the user's firstconversational activity includes identifying a first sub-stream of thestream of digital pictures of the user, wherein the first sub-stream ofthe stream of digital pictures of the user comprises the user's visualexpressions or communication in a first part of a simulatedconversation. In further embodiments, the detecting the user's firstconversational activity includes identifying a first sub-stream of thestream of digital sound samples of the user, wherein the firstsub-stream of the stream of digital sound samples of the user comprisesthe user's verbal expressions or communication in a first part of asimulated conversation.

In certain embodiments, the detecting the user's first conversationalactivity includes recognizing the user's speech or sound in the streamof digital sound samples of the user. In further embodiments, thedetecting the user's first conversational activity includes recognizingthe user's face or body part in the stream of digital pictures of theuser. In further embodiments, the detecting the user's firstconversational activity includes determining a beginning and an end ofthe user's first conversational activity.

In certain embodiments, the recording of the user's first conversationalactivity includes the user's visual expressions or communication in afirst part of a simulated conversation and the user's verbal expressionsor communication in the first part of the simulated conversation.

In some embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital soundsamples of the user and the recording of the first conversationparticipant's first conversational activity includes a first sub-streamof a stream of digital sound samples of the first conversationparticipant; wherein the comparing at least one portion of the recordingof the user's first conversational activity with at least one portion ofthe recording of the first conversation participant's firstconversational activity includes comparing at least one portion of thefirst sub-stream of the stream of digital sound samples of the user withat least one portion of the first sub-stream of the stream of digitalsound samples of the first conversation participant. In furtherembodiments, the portion of the first sub-stream of the stream ofdigital sound samples of the user includes one or more words, one ormore features, or one or more sound samples of the first sub-stream ofthe stream of digital sound samples of the user. In further embodiments,the portion of the first sub-stream of the stream of digital soundsamples of the first conversation participant includes one or morewords, one or more features, or one or more sound samples of the firstsub-stream of a stream of digital sound samples of the firstconversation participant.

In certain embodiments, the comparing at least one portion of therecording of the user's first conversational activity with at least oneportion of the recording of the first conversation participant's firstconversational activity includes comparing at least one word recognizedfrom the recording of the user's first conversational activity with atleast one word recognized from the recording of the first conversationparticipant's first conversational activity. In further embodiments, thecomparing at least one portion of the recording of the user's firstconversational activity with at least one portion of the recording ofthe first conversation participant's first conversational activityincludes comparing at least one sound feature from the recording of theuser's first conversational activity with at least one sound featurefrom the recording of the first conversation participant's firstconversational activity. In further embodiments, the comparing at leastone portion of the recording of the user's first conversational activitywith at least one portion of the recording of the first conversationparticipant's first conversational activity includes comparing at leastone sound sample from the recording of the user's first conversationalactivity with at least one sound sample from the recording of the firstconversation participant's first conversational activity. In furtherembodiments, the comparing at least one portion of the recording of theuser's first conversational activity with at least one portion of therecording of the first conversation participant's first conversationalactivity includes performing at least one of: amplitude adjustment,sample rate adjustment, noise reduction, or temporal alignment of one ormore sound samples in the recording of the user's first conversationalactivity or the recording of the first conversation participant's firstconversational activity.

In some embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital soundsamples of the user and the recording of the first conversationparticipant's first conversational activity includes a first sub-streamof a stream of digital sound samples of the first conversationparticipant, wherein the comparing at least one portion of the recordingof the user's first conversational activity with at least one portion ofthe recording of the first conversation participant's firstconversational activity includes performing at least one of: amplitudeadjustment; sample rate adjustment, noise reduction, or temporalalignment in the first sub-stream of the stream of digital sound samplesof the user or the first sub-stream of the stream of digital soundsamples of the first conversation participant.

In certain embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital picturesof the user and the recording of the first conversation participant'sfirst conversational activity includes a first sub-stream of a stream ofdigital pictures of the first conversation participant, wherein thecomparing at least one portion of the recording of the user's firstconversational activity with at least one portion of the recording ofthe first conversation participant's first conversational activityincludes comparing at least one portion of the first sub-stream of thestream of digital pictures of the user with at least one portion of thefirst sub-stream of the stream of digital pictures of the firstconversation participant. In further embodiments, the portion of thefirst sub-stream of the stream of digital pictures of the user includesone or more pictures, one or more regions of a picture, one or morefeatures of a picture, or one or more pixels of a picture of the firstsub-stream of the stream of digital pictures of the user. In furtherembodiments, the portion of the first sub-stream of the stream ofdigital pictures of the first conversation participant includes one ormore pictures, one or more regions of a picture, one or more features ofa picture, or one or more pixels of a picture of the first sub-stream ofthe stream of digital pictures of the first conversation participant.

In some embodiments, the comparing at least one portion of the recordingof the user's first conversational activity with at least one portion ofthe recording of the first conversation participant's firstconversational activity includes comparing at least one picture from therecording of the user's first conversational activity with at least onepicture from the recording of the first conversation participant's firstconversational activity. In further embodiments, the comparing at leastone portion of the recording of the user's first conversational activitywith at least one portion of the recording of the first conversationparticipant's first conversational activity includes comparing at leastone region of a picture from the recording of the user's firstconversational activity with at least one region of a picture from therecording of the first conversation participant's first conversationalactivity. In further embodiments, the comparing at least one portion ofthe recording of the user's first conversational activity with at leastone portion of the recording of the first conversation participant'sfirst conversational activity includes comparing at least one picturefeature from the recording of the user's first conversational activitywith at least one picture feature from the recording of the firstconversation participant's first conversational activity. In furtherembodiments, the comparing at least one portion of a recording of theuser's first conversational activity with at least one portion of therecording of the first conversation participant's first conversationalactivity includes comparing at least one pixel from the recording of theuser's first conversational activity with at least one pixel from therecording of the first conversation participant's first conversationalactivity. In further embodiments, the comparing at least one portion ofthe recording of the user's first conversational activity with at leastone portion of the recording of the first conversation participant'sfirst conversational activity includes performing at least one of: coloradjustment, size adjustment, transparency utilization, mask utilization,or temporal alignment of one or more pictures in the recording of theuser's first conversational activity or the recording of the firstconversation participant's first conversational activity.

In certain embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital picturesof the user and the recording of the first conversation participant'sfirst conversational activity includes a first sub-stream of a stream ofdigital pictures of the first conversation participant, wherein thecomparing at least one portion of the recording of the user's firstconversational activity with at least one portion of the recording ofthe first conversation participant's first conversational activityincludes performing at least one of: color adjustment, size adjustment,transparency utilization, mask utilization, or temporal alignment in thefirst sub-stream of the stream of digital pictures of the user or thefirst sub-stream of the stream of digital pictures of the firstconversation participant.

In some embodiments, the comparing at least one portion of the recordingof the user's first conversational activity with at least one portion ofthe recording of the first conversation participant's firstconversational activity includes comparing an extra information includedin the recording of the user's first conversational activity with anextra information included in the recording of the first conversationparticipant's first conversational activity. The extra information mayinclude a time information, a location information, an observedinformation, or a contextual information.

In certain embodiments, the determining that a similarity between atleast one portion of the recording of the user's first conversationalactivity and at least one portion of the recording of the firstconversation participant's first conversational activity exceeds asimilarity threshold includes determining that a similarity between anextra information included in the recording of the user's firstconversational activity and an extra information included in therecording of the first conversation participant's first conversationalactivity exceeds a similarity threshold. The extra information mayinclude a time information, a location information, an observedinformation, or a contextual information.

In some embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital soundsamples of the user and the recording of the first conversationparticipant's first conversational activity includes a first sub-streamof a stream of digital sound samples of the first conversationparticipant, wherein the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a similarity between at least oneportion of the first sub-stream of the stream of digital sound samplesof the user and at least one portion of the first sub-stream of thestream of digital sound samples of the first conversation participantexceeds a similarity threshold. In further embodiments, the portion ofthe first sub-stream of the stream of digital sound samples of the userincludes one or more words, one or more features, or one or more soundsamples of the first sub-stream of the stream of digital sound samplesof the user. In further embodiments, the portion of the first sub-streamof the stream of digital sound samples of the first conversationparticipant includes one or more words, one or more features, or one ormore sound samples of the first sub-stream of the stream of digitalsound samples of the first conversation participant.

In certain embodiments, the determining that a similarity between atleast one portion of the recording of the user's first conversationalactivity and at least one portion of the recording of the firstconversation participant's first conversational activity exceeds asimilarity threshold includes determining that a number or a percentageof matching words recognized from the recording of the user's firstconversational activity and from the recording of the first conversationparticipant's first conversational activity exceeds a threshold. Infurther embodiments, the matching words recognized from the recording ofthe user's first conversational activity and from the recording of thefirst conversation participant's first conversational activity arematched factoring in at least one of: an order of a word, a type of aword, an importance of a word, a semantic variation of a word, a conceptof a word, or a threshold for a difference in a word. In furtherembodiments, the determining that a similarity between at least oneportion of the recording of the user's first conversational activity andat least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingsound features from the recording of the user's first conversationalactivity and from the recording of the first conversation participant'sfirst conversational activity exceeds a threshold. In furtherembodiments, the matching sound features from the recording of theuser's first conversational activity and from the recording of the firstconversation participant's first conversational activity are matchedfactoring in at least one of: an order of a sound feature, a type of asound feature, an importance of a sound feature, or a threshold for adifference in a sound feature. In further embodiments, the determiningthat a similarity between at least one portion of the recording of theuser's first conversational activity and at least one portion of therecording of the first conversation participant's first conversationalactivity exceeds a similarity threshold includes determining that anumber or a percentage of matching sound samples from the recording ofthe user's first conversational activity and from the recording of thefirst conversation participant's first conversational activity exceeds athreshold. In further embodiments, the matching sound samples from therecording of the user's first conversational activity and from therecording of the first conversation participant's first conversationalactivity are matched factoring in at least one of: an order of a soundsample, an importance of a sound sample, or a threshold for a differencein a sound sample.

In some embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes recognizing a speech or a sound of a same person inthe at least one portion of the recording of the user's firstconversational activity and the at least one portion of the recording ofthe first conversation participant's first conversational activity.

In certain embodiments, the recording of the user's first conversationalactivity includes a first sub-stream of the stream of digital picturesof the user and the recording of the first conversation participant'sfirst conversational activity includes a first sub-stream of a stream ofdigital pictures of the first conversation participant, wherein thedetermining that a similarity between at least one portion of therecording of the user's first conversational activity and at least oneportion of the recording of the first conversation participant's firstconversational activity exceeds a similarity threshold includesdetermining that a similarity between at least one portion of the firstsub-stream of the stream of digital pictures of the user and at leastone portion of the first sub-stream of the stream of digital pictures ofthe first conversation participant exceeds a similarity threshold. Infurther embodiments, the portion of the first sub-stream of the streamof digital pictures of the user includes one or more pictures, one ormore regions of a picture, one or more features of a picture, or one ormore pixels of a picture of the first sub-stream of the stream ofdigital pictures of the user. In further embodiments, the portion of thefirst sub-stream of the stream of digital pictures of the firstconversation participant includes one or more pictures, one or moreregions of a picture, one or more features of a picture, or one or morepixels of a picture of the first sub-stream of a stream of digitalpictures of the first conversation participant.

In some embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingpictures from the recording of the user's first conversational activityand from the recording of the first conversation participant's firstconversational activity exceeds a threshold. In further embodiments, thematching pictures from the recording of the user's first conversationalactivity and from the recording of the first conversation participant'sfirst conversational activity are matched factoring in at least one of:an order of a picture, or a threshold for a difference in a picture. Infurther embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingregions of pictures from the recording of the user's firstconversational activity and from the recording of the first conversationparticipant's first conversational activity exceeds a threshold. Infurther embodiments, the matching regions of a picture from therecording of the user's first conversational activity and from therecording of the first conversation participant's first conversationalactivity are matched factoring in at least one of: a location of aregion, or a threshold for a difference in a region. In furtherembodiments, the determining that a similarity between at least oneportion of the recording of the user's first conversational activity andat least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingpicture features from the recording of the user's first conversationalactivity and from the recording of the first conversation participant'sfirst conversational activity exceeds a threshold. In furtherembodiments, the matching picture features from the recording of theuser's first conversational activity and from the recording of the firstconversation participant's first conversational activity are matchedfactoring in at least one of: a type of a picture feature, an importanceof a picture feature, a location of a picture feature in a region ofinterest, or a threshold for a difference in a picture feature. Infurther embodiments, the determining that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold includes determining that a number or a percentage of matchingpixels from the recording of the user's first conversational activityand from the recording of the first conversation participant's firstconversational activity exceeds a threshold. In further embodiments, thematching pixels from the recording of the user's first conversationalactivity and from the recording of the first conversation participant'sfirst conversational activity are matched factoring in at least one of:a location of a pixel in a region of interest, or a threshold for adifference in a pixel.

In certain embodiments, the determining that a similarity between atleast one portion of the recording of the user's first conversationalactivity and at least one portion of the recording of the firstconversation participant's first conversational activity exceeds asimilarity threshold includes recognizing a same person or object in theat least one portion of the recording of the user's first conversationalactivity and the at least one portion of the recording of the firstconversation participant's first conversational activity.

In some embodiments, the playing the at least one portion of therecording of the second conversation participant's first conversationalactivity is performed concurrently with at least one portion of theuser's first conversational activity. In further embodiments, therecording of the second conversation participant's first conversationalactivity includes a first sub-stream of a stream of digital pictures ofthe second conversation participant, wherein the playing the at leastone portion of the recording of the second conversation participant'sfirst conversational activity includes playing the at least one portionof the first sub-stream of the stream of digital pictures of the secondconversation participant. In further embodiments, the recording of thesecond conversation participant's first conversational activity includesa first sub-stream of a stream of digital sound samples of the secondconversation participant, wherein the playing the at least one portionof the recording of the second conversation participant's firstconversational activity includes playing the at least one portion of thefirst sub-stream of the stream of digital sound samples of the secondconversation participant.

In certain embodiments, the stored plurality of rounds of conversationalexchange include a second round of conversational exchange, the secondround of conversational exchange comprising a recording of a firstconversation participant's second conversational activity and arecording of a second conversation participant's second conversationalactivity. The non-transitory computer storage medium and/or the methodfurther comprise: detecting the user's second conversational activityfrom at least one of the stream of digital pictures of the user or thestream of digital sound samples of the user, the detecting performed bythe one or more processor circuits of the server. The non-transitorycomputer storage medium and/or the method further comprise: comparing atleast one portion of a recording of the user's second conversationalactivity with at least one portion of the recording of the firstconversation participant's second conversational activity, the comparingperformed by the one or more processor circuits of the server. Thenon-transitory computer storage medium and/or the method furthercomprise: determining that a similarity between at least one portion ofthe recording of the user's second conversational activity and at leastone portion of the recording of the first conversation participant'ssecond conversational activity exceeds a similarity threshold, thedetermining performed by the one or more processor circuits of theserver. The non-transitory computer storage medium and/or the methodfurther comprise: playing at least one portion of the recording of thesecond conversation participant's second conversational activity by thedisplay and the sound-producing device of the user's computing device,the playing caused by the one or more processor circuits of the server.In further embodiments, the first round of conversational exchange isconnected to the second round of conversational exchange by aconnection. The connection between the first round of conversationalexchange and the second round of conversational exchange may include orbe associated with at least one of: an occurrence count, a weight, aparameter, or a data. In further embodiments, the stored plurality ofrounds of conversational exchange are organized into a neural network,and wherein the first round of conversational exchange is stored into afirst node of the neural network and the second round of conversationalexchange is stored into a second node of the neural network. The firstnode and the second node may be connected by a connection. The firstnode may be part of a first layer of the neural network and the secondnode may be part of a second layer of the neural network. In furtherembodiments, the stored plurality of rounds of conversational exchangeare organized into a graph, and wherein the first round ofconversational exchange is stored into a first node of the graph and thesecond round of conversational exchange is stored into a second node ofthe graph. In further embodiments, the first node and the second nodemay be connected by a connection. In further embodiments, the storedplurality of rounds of conversational exchange are organized into acollection of sequences, and wherein the first round of conversationalexchange is stored into a first node of a sequence of the collection ofsequences and the second round of conversational exchange is stored intoa second node of the sequence of the collection of sequences. In furtherembodiments, the stored plurality of rounds of conversational exchangeare organized into a sequence, and wherein the first round ofconversational exchange is stored into a first node of the sequence andthe second round of conversational exchange is stored into a second nodeof the sequence.

In some embodiments, the playing the at least one portion of therecording of the second conversation participant's second conversationalactivity is performed concurrently with the at least one portion of theuser's second conversational activity. In further embodiments, theplaying the at least one portion of the recording of the secondconversation participant's second conversational activity is performedsubsequent to the at least one portion of the user's firstconversational activity.

In certain embodiments, the playing the at least one portion of therecording of the second conversation participant's second conversationalactivity includes transitioning from the at least one portion of therecording of the second conversation participant's first conversationalactivity to the at least one portion of the recording of the secondconversation participant's second conversational activity. In furtherembodiments, the transitioning from the at least one portion of therecording of the second conversation participant's first conversationalactivity to the at least one portion of the recording of the secondconversation participant's second conversational activity includes atleast one of: moving, centering, aligning, resizing, or transforming oneor more pictures of the recording of the second conversationparticipant's first conversational activity and one or more pictures ofthe recording of the second conversation participant's secondconversational activity. In further embodiments, the transitioning fromthe at least one portion of the recording of the second conversationparticipant's first conversational activity to the at least one portionof the recording of the second conversation participant's secondconversational activity includes adjusting a lighting or a color of oneor more pictures of the recording of the second conversationparticipant's first conversational activity and one or more pictures ofthe recording of the second conversation participant's secondconversational activity. In further embodiments, the transitioning fromthe at least one portion of the recording of the second conversationparticipant's first conversational activity to the at least one portionof the recording of the second conversation participant's secondconversational activity includes a cut or a dissolve between one or morepictures of the recording of the second conversation participant's firstconversational activity and one or more pictures of the recording of thesecond conversation participant's second conversational activity. Infurther embodiments, the transitioning from the at least one portion ofthe recording of the second conversation participant's firstconversational activity to the at least one portion of the recording ofthe second conversation participant's second conversational activityincludes morphing of one or more pictures of the recording of the secondconversation participant's first conversational activity and one or morepictures of the recording of the second conversation participant'ssecond conversational activity.

In some embodiments, the playing the at least one portion of therecording of the second conversation participant's second conversationalactivity includes bridging between the at least one portion of therecording of the second conversation participant's first conversationalactivity and the at least one portion of the recording of the secondconversation participant's second conversational activity. In furtherembodiments, the bridging between the at least one portion of therecording of the second conversation participant's first conversationalactivity and the at least one portion of the recording of the secondconversation participant's second conversational activity includesinterpolation, inbetweening, extrapolation, or picture generationbetween one or more pictures of the recording of the second conversationparticipant's first conversational activity and one or more pictures ofthe recording of the second conversation participant's secondconversational activity. In further embodiments, the bridging betweenthe at least one portion of the recording of the second conversationparticipant's first conversational activity and the at least one portionof the recording of the second conversation participant's secondconversational activity includes playing or replaying one or morepictures of the recording of the second conversation participant's firstconversational activity.

In some aspects, the disclosure relates to a system for usingartificially intelligent interactive memories. The system may beimplemented at least in part on one or more computing devices. In someembodiments, the system comprises one or more processor circuits. Thesystem may further include a memory unit, coupled to the one or moreprocessor circuits, that stores a plurality of rounds of conversationalexchange including a first round of conversational exchange; the firstround of conversational exchange comprising a recording of a firstconversation participant's first conversational activity and a recordingof a second conversation participant's first conversational activity.The system may further include a picture-capturing device, coupled tothe one or more processor circuits, configured to capture a stream ofdigital pictures of a user. The system may further include asound-capturing device, coupled to the one or more processor circuits,configured to capture a stream of digital sound samples of the user Theone or more processor circuits may be configured to: detect the user'sfirst conversational activity from at least one of the stream of digitalpictures of the user or the stream of digital sound samples of the user.The one or more processor circuits may be further configured to: compareat least one portion of a recording of the user's first conversationalactivity with at least one portion of the recording of the firstconversation participant's first conversational activity. The one ormore processor circuits may be further configured to: determine that asimilarity between at least one portion of the recording of the user'sfirst conversational activity and at least one portion of the recordingof the first conversation participant's first conversational activityexceeds a similarity threshold. The one or more processor circuits maybe further configured to: cause a display and a sound-producing deviceto play at least one portion of the recording of the second conversationparticipant's first conversational activity.

In certain embodiments, the one or more processor circuits, the memoryunit, the picture-capturing device, the sound-capturing device, thedisplay, and the sound-producing device of the system are included in asingle device. In further embodiments, at least one of: the one or moreprocessor circuits or the memory unit of the system are included in aserver, and wherein the picture-capturing device, the sound-capturingdevice, the display, and the sound-producing device of the system areincluded in a user device, the user device coupled to the server via anetwork.

In some embodiments, the stored plurality of rounds of conversationalexchange include a second round of conversational exchange, the secondround of conversational exchange comprising a recording of a firstconversation participant's second conversational activity and arecording of a second conversation participant's second conversationalactivity. The one or more processor circuits may be further configuredto: detect the user's second conversational activity from at least oneof the stream of digital pictures of the user or the stream of digitalsound samples of the user. The one or more processor circuits may befurther configured to: compare at least one portion of a recording ofthe user's second conversational activity with at least one portion ofthe recording of the first conversation participant's secondconversational activity. The one or more processor circuits may befurther configured to: determine that a similarity between at least oneportion of the recording of the user's second conversational activityand at least one portion of the recording of the first conversationparticipant's second conversational activity exceeds a similaritythreshold The one or more processor circuits may be further configuredto: cause a display and a sound-producing device to play at least oneportion of the recording of the second conversation participant's secondconversational activity. In further embodiments, the playing the atleast one portion of the recording of the second conversationparticipant's second conversational activity is performed concurrentlywith the at least one portion of the user's second conversationalactivity. In further embodiments, the playing the at least one portionof the recording of the second conversation participant's secondconversational activity is performed subsequent to the at least oneportion of the user's first conversational activity. In furtherembodiments, the playing the at least one portion of the recording ofthe second conversation participant's second conversational activityincludes transitioning from the at least one portion of the recording ofthe second conversation participant's first conversational activity tothe at least one portion of the recording of the second conversationparticipant's second conversational activity. In further embodiments,the playing the at least one portion of the recording of the secondconversation participant's second conversational activity includesbridging between the at least one portion of the recording of the secondconversation participant's first conversational activity and the atleast one portion of the recording of the second conversationparticipant's second conversational activity.

In some aspects, the disclosure relates to a non-transitory computerstorage medium having a computer program stored thereon, the programcomprising instructions that when executed by one or more computingdevices cause the one or more computing devices to perform operationscomprising: accessing a memory unit that stores a plurality of rounds ofconversational exchange including a first round of conversationalexchange, the first round of conversational exchange comprising arecording of a first conversation participant's first conversationalactivity and a recording of a second conversation participant's firstconversational activity. The operations may further include capturing astream of digital pictures of a user by a picture-capturing device. Theoperations may further include capturing a stream of digital soundsamples of the user by a sound-capturing device. The operations mayfurther include detecting the user's first conversational activity fromat least one of the stream of digital pictures of the user or the streamof digital sound samples of the user. The operations may further includecomparing at least one portion of a recording of the user's firstconversational activity with at least one portion of the recording ofthe first conversation participant's first conversational activity. Theoperations may further include determining that a similarity between atleast one portion of the recording of the user's first conversationalactivity and at least one portion of the recording of the firstconversation participant's first conversational activity exceeds asimilarity threshold. The operations may further include playing atleast one portion of the recording of the second conversationparticipant's first conversational activity by a display and asound-producing device.

In some aspects, the disclosure relates to a method comprising: (a)accessing a memory unit that stores a plurality of rounds ofconversational exchange including a first round of conversationalexchange, the first round of conversational exchange comprising arecording of a first conversation participant's first conversationalactivity and a recording of a second conversation participant's firstconversational activity, the accessing of (a) performed by one or moreprocessor circuits. The method may further include (b) capturing astream of digital pictures of a user by a picture-capturing device thatis coupled to the one or more processor circuits. The method may furtherinclude (c) capturing a stream of digital sound samples of the user by asound-capturing device that is coupled to the one or more processorcircuits. The method may further include (d) detecting the user's firstconversational activity from at least one of the stream of digitalpictures of the user or the stream of digital sound samples of the user,the detecting of (d) performed by the one or more processor circuits.The method may further include (e) comparing at least one portion of arecording of the user's first conversational activity with at least oneportion of the recording of the first conversation participant's firstconversational activity, the comparing of (e) performed by the one ormore processor circuits. The method may further include (f) determiningthat a similarity between at least one portion of the recording of theuser's first conversational activity and at least one portion of therecording of the first conversation participant's first conversationalactivity exceeds a similarity threshold, the determining of (f)performed by the one or more processor circuits. The method may furtherinclude (g) playing at least one portion of the recording of the secondconversation participant's first conversational activity by a displayand a sound-producing device, the playing of (g) caused by the one ormore processor circuits.

The operations or steps of the non-transitory computer storage mediumand/or the method may be performed by any of the elements of the abovedescribed system as applicable. The non-transitory computer storagemedium and/or the method may include any of the operations, steps, andembodiments of the above described system as applicable as well as thefollowing embodiments.

In certain embodiments, the one or more processor circuits, the memoryunit, the picture-capturing device, the sound-capturing device, thedisplay, and the sound-producing device of the system are included in asingle device. In further embodiments, at least one of: the one or moreprocessor circuits or the memory unit of the system are included in aserver, and wherein the picture-capturing device, the sound-capturingdevice, the display, and the sound-producing device of the system areincluded in a user device, the user device coupled to the server via anetwork.

In some embodiments, the stored plurality of rounds of conversationalexchange include a second round of conversational exchange, the secondround of conversational exchange comprising a recording of a firstconversation participant's second conversational activity and arecording of a second conversation participant's second conversationalactivity. The non-transitory computer storage medium and/or the methodfurther comprise: detecting the user's second conversational activityfrom at least one of the stream of digital pictures of the user or thestream of digital sound samples of the user, the detecting performed bythe one or more processor circuits. The non-transitory computer storagemedium and/or the method further comprise: comparing at least oneportion of a recording of the user's second conversational activity withat least one portion of the recording of the first conversationparticipant's second conversational activity, the comparing performed bythe one or more processor circuits. The non-transitory computer storagemedium and/or the method further comprise: determining that a similaritybetween at least one portion of the recording of the user's secondconversational activity and at least one portion of the recording of thefirst conversation participant's second conversational activity exceedsa similarity threshold, the determining performed by the one or moreprocessor circuits. The non-transitory computer storage medium and/orthe method further comprise: playing at least one portion of therecording of the second conversation participant's second conversationalactivity by the display and the sound-producing device, the playingcaused by the one or more processor circuits. In further embodiments,the playing the at least one portion of the recording of the secondconversation participant's second conversational activity is performedconcurrently with the at least one portion of the user's secondconversational activity. In further embodiments, the playing the atleast one portion of the recording of the second conversationparticipant's second conversational activity is performed subsequent tothe at least one portion of the user's first conversational activity. Infurther embodiments, the playing the at least one portion of therecording of the second conversation participant's second conversationalactivity includes transitioning from the at least one portion of therecording of the second conversation participant's first conversationalactivity to the at least one portion of the recording of the secondconversation participant's second conversational activity. In furtherembodiments, the playing the at least one portion of the recording ofthe second conversation participant's second conversational activityincludes bridging between the at least one portion of the recording ofthe second conversation participant's first conversational activity andthe at least one portion of the recording of the second conversationparticipant's second conversational activity.

In some aspects, the disclosure relates to a system for usingartificially intelligent interactive memories. The system may beimplemented at least in part on one or more computing devices. In someembodiments, the system comprises one or more processor circuits. Thesystem may further include a memory unit, coupled to the one or moreprocessor circuits, that stores a plurality of rounds of conversationalexchange including a first round of conversational exchange, the firstround of conversational exchange comprising recordings of a firstconversation participant's first and second conversational activitiesand recordings of a second conversation participant's first and secondconversational activities. The system may further include apicture-capturing device, coupled to the one or more processor circuits,configured to capture a stream of digital pictures of a user. The systemmay further include a sound-capturing device, coupled to the one or moreprocessor circuits, configured to capture a stream of digital soundsamples of the user. The one or more processor circuits may beconfigured to: detect the user's first conversational activity from atleast one of the stream of digital pictures of the user or the stream ofdigital sound samples of the user. The one or more processor circuitsmay be further configured to: compare at least one portion of arecording of the user's first conversational activity with at least oneportion of the recording of the first conversation participant's firstconversational activity. The one or more processor circuits may befurther configured to: determine that a similarity between at least oneportion of the recording of the user's first conversational activity andat least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold. The one or more processor circuits may be further configuredto: cause a display and a sound-producing device to play at least oneportion of the recording of the second conversation participant's firstconversational activity.

In certain embodiments, the first conversation participant's firstconversational activity is correlated with the second conversationparticipant's first conversational activity and the first conversationparticipant's second conversational activity is correlated with thesecond conversation participant's second conversational activity.

In some embodiments, the one or more processor circuits may be furtherconfigured to: cause the display and the sound-producing device to playat least one portion of the recording of the second conversationparticipant's second conversational activity. In further embodiments,the playing the at least one portion of the recording of the secondconversation participant's second conversational activity is performedconcurrently with the at least one portion of the user's secondconversational activity. In further embodiments, the playing the atleast one portion of the recording of the second conversationparticipant's second conversational activity is performed subsequent tothe at least one portion of the user's first conversational activity.

In some aspects, the disclosure relates to a non-transitory computerstorage medium having a computer program stored thereon, the programcomprising instructions that when executed by one or more computingdevices cause the one or more computing devices to perform operationscomprising: accessing a memory unit that stores a plurality of rounds ofconversational exchange including a first round of conversationalexchange, the first round of conversational exchange comprisingrecordings of a first conversation participant's first and secondconversational activities and recordings of a second conversationparticipant's first and second conversational activities. The operationsmay further include capturing a stream of digital pictures of a user bya picture-capturing device. The operations may further include capturinga stream of digital sound samples of the user by a sound-capturingdevice. The operations may further include detecting the user's firstconversational activity from at least one of the stream of digitalpictures of the user or the stream of digital sound samples of the user.The operations may further include comparing at least one portion of arecording of the user's first conversational activity with at least oneportion of the recording of the first conversation participant's firstconversational activity. The operations may further include determiningthat a similarity between at least one portion of the recording of theuser's first conversational activity and at least one portion of therecording of the first conversation participant's first conversationalactivity exceeds a similarity threshold. The operations may furtherinclude playing at least one portion of the recording of the secondconversation participant's first conversational activity by a displayand a sound-producing device.

In some aspects, the disclosure relates to a method comprising: (a)accessing a memory unit that stores a plurality of rounds ofconversational exchange including a first round of conversationalexchange, the first round of conversational exchange comprisingrecordings of a first conversation participant's first and secondconversational activities and recordings of a second conversationparticipant's first and second conversational activities, the accessingof (a) performed by one or more processor circuits The method mayfurther include (b) capturing a stream of digital pictures of a user bya picture-capturing device that is coupled to the one or more processorcircuits. The method may further include (c) capturing a stream ofdigital sound samples of the user by a sound-capturing device that iscoupled to the one or more processor circuits. The method may furtherinclude (d) detecting the user's first conversational activity from atleast one of the stream of digital pictures of the user or the stream ofdigital sound samples of the user, the detecting of (d) performed by theone or more processor circuits. The method may further include (e)comparing at least one portion of a recording of the user's firstconversational activity with at least one portion of the recording ofthe first conversation participant's first conversational activity, thecomparing of (e) performed by the one or more processor circuits. Themethod may further include (f) determining that a similarity between atleast one portion of the recording of the user's first conversationalactivity and at least one portion of the recording of the firstconversation participant's first conversational activity exceeds asimilarity threshold, the determining of (f) performed by the one ormore processor circuits. The method may further include (g) playing atleast one portion of the recording of the second conversationparticipant's first conversational activity by a display and asound-producing device, the playing of (g) caused by the one or moreprocessor circuits.

The operations or steps of the non-transitory computer storage mediumand/or the method may be performed by any of the elements of the abovedescribed system as applicable. The non-transitory computer storagemedium and/or the method may include any of the operations, steps, andembodiments of the above described system as applicable as well as thefollowing embodiments.

In certain embodiments, the first conversation participant's firstconversational activity is correlated with the second conversationparticipant's first conversational activity and the first conversationparticipant's second conversational activity is correlated with thesecond conversation participant's second conversational activity.

In some embodiments, the non-transitory computer storage medium and/orthe method further comprise: playing at least one portion of therecording of the second conversation participant's second conversationalactivity by the display and the sound-producing device, the playingcaused by the one or more processor circuits. In further embodiments,the playing the at least one portion of the recording of the secondconversation participant's second conversational activity is performedconcurrently with the at least one portion of the user's secondconversational activity. In further embodiments, the playing the atleast one portion of the recording of the second conversationparticipant's second conversational activity is performed subsequent tothe at least one portion of the user's first conversational activity.

Other features and advantages of the disclosure will become apparentfrom the following description, including the claims and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of Computing Device 70 that canprovide processing capabilities used in some of the disclosedembodiments.

FIG. 2A illustrates an embodiment of utilizing System for Learning AIIMs100 in a dedicated device.

FIG. 2B illustrates an embodiment of internal structure of System forLearning AIIMs 100 in a dedicated device.

FIG. 3A illustrates an embodiment of Activity Detector 160 extracting orfiltering persons and/or objects of interest.

FIG. 3B illustrates an embodiment of Activity Detector 160 extracting orfiltering speech and/or sounds of interest.

FIG. 4 illustrates an embodiment of Knowledge Structuring Unit 110.

FIG. 5 illustrates another embodiment of Knowledge Structuring Unit 110.

FIG. 6A-6F illustrate variety of possible arrangements of ConversationalActivities 210 that can be stored in Rounds of Conversational Exchange200.

FIG. 7A-7E illustrate additional variety of possible arrangements ofConversational Activities 210 that can be stored in Rounds ofConversational Exchange 200.

FIG. 8A-8C illustrate some embodiments of Extra Information 250.

FIG. 9A illustrates an embodiment of utilizing System for Learning AIIMs100 implemented as a network service.

FIG. 9B illustrates an embodiment of internal structure of System forLearning AIIMs 100 implemented as a network service.

FIG. 10A illustrates an embodiment of utilizing System for LearningAIIMs 100 embedded in Host Device 98.

FIG. 10B illustrates an embodiment of internal structure of System forLearning AIIMs 100 embedded in Host Device 98.

FIG. 11 illustrates various artificial intelligence methods, systems,and/or models that can be utilized in AIIM embodiments.

FIG. 12A-12C illustrate examples of interconnected Rounds ofConversational Exchange 200 and updating weights of Connections 853.

FIG. 13 illustrates an example of learning Rounds of ConversationalExchange 200 using Neural Network 130 a.

FIG. 14 illustrates an example of learning Rounds of ConversationalExchange 200 using Neural Network 130 a comprising shortcut Connections853.

FIG. 15 illustrates an example of learning Rounds of ConversationalExchange 200 using Graph 130 b.

FIG. 16 illustrates an example of learning Rounds of ConversationalExchange 200 using Collection of Sequences 130 c.

FIG. 17 illustrates a flow chart diagram of an embodiment of a method6100 for learning AIIMs.

FIG. 18A illustrates an embodiment of System for Using AIIMs 500implemented on User Device 80,

FIG. 18B illustrates an embodiment of internal structure of System forUsing AIIMs 500 implemented on User Device 80.

FIG. 19 illustrates an embodiment of internal structure of System forUsing AIIMs 500 implemented as a network service.

FIG. 20 illustrates an example of selecting a path of Rounds ofConversational Exchange 200 (or Conversational Activities 210 therein)through Neural Network 130 a.

FIG. 21 illustrates another example of selecting a path of Rounds ofConversational Exchange 200 (or Conversational Activities 210 therein)through Neural Network 130 a.

FIG. 22 illustrates an example of selecting a path of Rounds ofConversational Exchange 200 (or Conversational Activities 210 therein)through Graph 130 b.

FIG. 23 illustrates another example of selecting a path of Rounds ofConversational Exchange 200 (or Conversational Activities 210 therein)through Graph 130 b.

FIG. 24 illustrates an example of selecting a Sequence 133 of Rounds ofConversational Exchange 200 (or Conversational Activities 210 therein)in Collection of Sequences 130 c.

FIG. 25 illustrates an example of selecting Rounds of ConversationalExchange 200 (or Conversational Activities 210 therein) in a singleSequence 133.

FIG. 26 illustrates a flow chart diagram of an embodiment of method 6200for using AIIMs.

Like reference numerals in different figures indicate like elements.Horizontal or vertical “ . . . ” or other such indicia may be used toindicate additional instances of the same type of element. n, m, orother such letters or indicia represent integers or other sequentialnumbers that follow the sequence where they are indicated. It should benoted that n, m, and/or other such letters or indicia may representdifferent numbers in different elements even where the elements aredepicted in the same figure. In general, n, m, and/or other such lettersor indicia follow the immediate sequence and/or context where they areindicated. Any of these or other such indicia may be usedinterchangeably according to the context and space available. Thedrawings are not necessarily to scale, with emphasis instead beingplaced upon illustrating the embodiments, principles, and concepts ofthe disclosure. A line or arrow between any of the disclosed elementscomprises an interface that enables the coupling, connection, and/orinteraction between the elements. Arrows are used for enhancedillustration of the concepts and do not require the indicateddirections. Therefore, any arrow can be replaced with an undirected linein alternate embodiments. For clarity of illustration, white coloredstreams and sub-streams of digital pictures and sound samples areassociated with one conversation participant, whereas, gray coloredstreams and sub-streams of digital pictures and sound samples areassociated with another or counterpart conversation participant.

DETAILED DESCRIPTION

The disclosed devices, systems, and methods for learning and usingartificially intelligent interactive memories comprise apparatuses,systems, methods, features, functionalities, and/or applications forlearning conversations among two or more conversation participants andstoring this knowledge in a knowledgebase (i.e. neural network, graph,sequences, etc.). Then, using this stored knowledge, the discloseddevices, systems, and methods enable a user to simulate a conversationwith an artificially intelligent conversation participant. The discloseddevices, systems, and methods for learning and using artificiallyintelligent interactive memories, any of their elements, any of theirembodiments, or a combination thereof can generally be referred to asAIIM, AIIM application, or as other similar name or reference.

Referring now to FIG. 1, an embodiment is illustrated of ComputingDevice 70 (also referred to simply as computing device or other similarname or reference, etc.) that can provide processing capabilities usedin some embodiments of the forthcoming disclosure. Later describeddevices and systems, in combination with processing capabilities ofComputing Device 70, enable learning and using artificially intelligentinteractive memories and/or other functionalities described herein.Various embodiments of the disclosed devices, systems, and/or methodsinclude hardware, functions, logic, programs, and/or a combinationthereof that can be provided or implemented on any type or form ofcomputing or other device such as a mobile device, a computer, acomputing capable telephone, a server, a cloud device, a gaming device,a television device, a digital camera, a GPS receiver, a media player,an embedded device, a supercomputer, a wearable device, an implantabledevice, or any other type or form of computing or other device capableof performing the operations described herein.

In some designs, Computing Device 70 comprises hardware, processingtechniques or capabilities, programs, or a combination thereof.Computing Device 70 includes one or more central processing units, whichmay also be referred to as processors 11. Processor 11 includes one ormore memory ports 10 and/or one or more input-output ports, alsoreferred to as I/O ports 15, such as I/O ports 15A and 15B. Processor 11may be special or general purpose. Computing Device 70 may furtherinclude memory 12, which can be connected to the remainder of thecomponents of Computing Device 70 via bus 5. Memory 12 can be connectedto processor 11 via memory port 10. Computing Device 70 may also includedisplay device 21 such as a monitor, projector, glasses, and/or otherdisplay device. Computing Device 70 may also include Human-machineInterface 23 such as a keyboard, a pointing device, a mouse, atouchscreen, a joystick, and/or other input device that can be connectedwith the remainder of the Computing Device 70 components via I/O control22. In some implementations, Human-machine Interface 23 can be connectedwith bus 5 or directly connected with specific components of ComputingDevice 70. Computing Device 70 may include additional elements, such asone or more input/output devices 13. Processor 11 may include or beinterfaced with cache memory 14. Storage 27 may include memory, whichprovides an operating system, also referred to as OS 17, additionalapplication programs 18 operating on OS 17, and/or data space 19 inwhich additional data or information can be stored. Alternative memorydevice 16 can be connected to the remaining components of ComputingDevice 70 via bus 5. Network interface 25 can also be connected with bus5 and be used to communicate with external computing devices via anetwork. Some or all described elements of Computing Device 70 can bedirectly or operatively connected or coupled with each other using anyother connection means known in art. Other additional elements may beincluded as needed, or some of the disclosed ones may be excluded, or acombination thereof may be utilized in alternate implementations ofComputing Device 70.

Processor 11 includes any logic circuitry that can respond to or processinstructions fetched from memory 12 or other element. Processor 11 mayalso include any combination of hardware and/or processing techniques orcapabilities for implementing or executing logic functions or programs.Processor 11 may include a single core or a multi core processor.Processor 11 includes the functionality for loading operating system 17and operating any application programs 18 thereon. In some embodiments,Processor 11 can be provided in a microprocessing or a processing unit,such as, for example, Snapdragon processor produced by Qualcomm Inc.,processor by Intel Corporation of Mountain View, Calif., processormanufactured by Motorola Corporation of Schaumburg, Ill.; processormanufactured by Transmeta Corporation of Santa Clara, Calif.; theRS/6000 processor, processor manufactured by International BusinessMachines of White Plains, N.Y.; processor manufactured by Advanced MicroDevices of Sunnyvale, Calif., or any computing unit for performingsimilar functions. In other embodiments, processor 11 can be provided ina graphics processor unit (GPU), visual processor unit (VPU), or otherhighly parallel processing unit or circuit such as, for example, nVidiaGeForce line of GPUs, AMD Radeon line of GPUs, and/or others. Such GPUsor other highly parallel processing units may provide superiorperformance in processing operations on later described neural networks.Computing Device 70 can be based on one or more of these or otherprocessors capable of operating as described herein.

Memory 12 includes one or more memory chips capable of storing data andallowing any storage location to be accessed by processor 11, such asStatic random access memory (SRAM), Flash memory, Burst SRAM orSynchBurst SRAM (BSRAM), Dynamic random access memory (DRAM), Fast PageMode DRAM (FPM DRAM), Enhanced DRAM (EDRAM), Extended Data Output RAM(EDO RAM), Extended Data Output DRAM (EDO DRAM), Burst Extended DataOutput DRAM (BEDO DRAM), Enhanced DRAM (EDRAM), synchronous DRAM(SDRAM), JEDEC SRAM, PC100 SDRAM, Double Data Rate SDRAM (DDR SDRAM),Enhanced SDRAM (ESDRAM), SyncLink DRAM (SLDRAM), Direct Rambus DRAM(DRDRAM), Ferroelectric RAM (FRAM), and/or others. Memory 12 can bebased on any of the above described memory chips, or any other availablememory chips capable of operating as described herein. In someembodiments, processor 11 can communicate with memory 12 via a systembus 5. In other embodiments, processor 11 can communicate directly withmemory 12 via a memory port 10.

Processor 11 can communicate directly with cache memory 14 via aconnection means such as a secondary bus which may also sometimes bereferred to as a backside bus. In some embodiments, processor 11 cancommunicate with cache memory 14 using the system bus 5. Cache memory 14may typically have a faster response time than main memory 12 and caninclude a type of memory which is considered faster than main memory 12,such as for example SRAM, BSRAM, or EDRAM. Cache memory includes anystructure such as multilevel caches, for example. In some embodiments,processor 11 can communicate with one or more I/O devices 13 via asystem bus 5. Various busses can be used to connect processor 11 to anyof the I/O devices 13, such as a VESA VL bus, an ISA bus, an EISA bus, aMicroChannel Architecture (MCA) bus, a PCI bus, a PCI-X bus, aPCI-Express bus, a NuBus, and/or others. In some embodiments, processor11 can communicate directly with I/O device 13 via HyperTransport, RapidI/O, or InfiniBand. In further embodiments, local busses and directcommunication can be mixed. For example, processor 11 can communicatewith an I/O device 13 using a local interconnect bus and communicatewith another I/O device 13 directly. Similar configurations can be usedfor any other components described herein.

Computing Device 70 may further include alternative memory such as a SDmemory slot, a USB memory stick, an optical drive such as a CD-ROMdrive, a CD-R/RW drive, a DVD-ROM drive or a BlueRay disc, a hard-drive,and/or any other device comprising non-volatile memory suitable forstoring data or installing application programs, Computing Device 70 mayfurther include a storage device 27 comprising any type or form ofnon-volatile memory for storing an operating system (OS) such as anytype or form of Windows OS, Mac OS, Unix OS, Linux OS, Android OS,iPhone OS, mobile version of Windows OS, an embedded OS, or any other OSthat can operate on Computing Device 70. Computing Device 70 may alsoinclude application programs 18, and/or data space 19 for storingadditional data or information. In some embodiments, alternative memory16 can be used as or similar to storage device 27, Additionally, OS 17and/or application programs 18 can be operable from a bootable medium,such as for example, a flash drive, a micro SD card, a bootable CD orDVD, and/or other bootable medium.

Application Program 18 (also referred to as program, computer program,application, script, code, etc.) comprises instructions that can providefunctionality when executed by processor 11. Application program 18 canbe implemented in a high-level procedural or object-oriented programminglanguage, or in a low-level machine or assembly language. Any languageused can be compiled, interpreted, or otherwise translated into machinelanguage. Application program 18 can be deployed in any form includingas a stand-alone program or as a module, component, subroutine, or otherunit suitable for use in a computing system, Application program 18 doesnot necessarily correspond to a file in a file system. A program can bestored in a portion of a file that may hold other programs or data, in asingle file dedicated to the program, or in multiple files (i.e. filesthat store one or more modules, sub programs, or portions of code,etc.). Application program 18 can be deployed to be executed on onecomputer or on multiple computers (i.e. cloud, distributed, or parallelcomputing, etc.), or at one site or distributed across multiple sitesinterconnected by a communication network.

Network interface 25 can be utilized for interfacing Computing Device 70with other devices via a network through a variety of connectionsincluding standard telephone lines, wired or wireless connections, LANor WAN links (i.e. 802.11, T1, T3, 56 kb, X.25, etc.), broadbandconnections (i.e. ISDN, Frame Relay, ATM, etc.), or a combinationthereof. Examples of networks include the Internet, an intranet, anextranet, a local area network (LAN), a wide area network (WAN), apersonal area network (PAN), a home area network (HAN), a campus areanetwork (CAN), a metropolitan area network (MAN), a global area network(GAN), a storage area network (SAN), virtual network, a virtual privatenetwork (VPN), Bluetooth network, a wireless network, a wireless LAN, aradio network, a HomePNA, a power line communication network, a G.hnnetwork, an optical fiber network, an Ethernet network, an activenetworking network, a client-server network, a peer-to-peer network, abus network, a star network, a ring network, a mesh network, a star-busnetwork, a tree network, a hierarchical topology network, and/or othernetworks known in art. Network interface 25 may include a built-innetwork adapter, network interface card, PCMCIA network card, card busnetwork adapter, wireless network adapter, Bluetooth network adapter,WiFi network adapter, USB network adapter, modem, and/or any otherdevice suitable for interfacing Computing Device 70 with any type ofnetwork capable of communication and/or operations described herein.

Still referring to FIG. 1, I/O devices 13 may be present in variousshapes or forms in Computing Device 70. Examples of I/O device 13capable of input include a joystick, a keyboard, a mouse, a trackpad, atrackpoint, a touchscreen, a trackball, a microphone, a drawing tablet,a glove, a tactile input device, a still or video camera, and/or otherinput device. Examples of I/O device 13 capable of output include avideo display, a touchscreen, a projector, a glasses, a speaker, atactile output device, and/or other output device. Examples of I/Odevice 13 capable of input and output include a disk drive, an opticalstorage device, a modem, a network card, and/or other input/outputdevice. I/O device 13 can be interfaced with processor 11 via an I/Oport 15, for example. I/O device 13 can also be controlled by I/Ocontrol 22 in some implementations. I/O control 22 may control one ormore I/O devices such as Human-machine interface 23 (i.e. keyboard,pointing device, touchscreen, joystick, mouse, optical pen, etc.). I/Ocontrol 22 enables any type or form of a device such as, for example, avideo camera or microphone to be interfaced with other components ofComputing Device 70. Furthermore, I/O device 13 may also provide storagesuch as or similar to storage 27, and/or alternative memory such as orsimilar to alternative memory 16 in some implementations.

An output interface such as a graphical user interface, an acousticaloutput interface, a tactile output interface, any device driver (i.e.audio, video, or other driver), and/or other output interface or systemcan be utilized to process output from elements of Computing Device 70for conveyance on an output device such as Display 21. In some aspects,Display 21 or other output device itself may include an output interfacefor processing output from elements of Computing Device 70. Further, aninput interface such as a keyboard listener, a touchscreen listener, amouse listener, any device driver (i.e. audio, video, keyboard, mouse,touchscreen, or other driver), a speech recognizer, a video interpreter,and/or other input interface or system can be utilized to process inputfrom Human-machine Interface 23 or other input device for use byelements of Computing Device 70. In some aspects, Human-machineInterface 23 or other input device itself may include an input interfacefor processing input for use by elements of Computing Device 70.

Computing Device 70 may include or be connected to multiple displaydevices 21. Display devices 21 can each be of the same or different typeor form. Computing Device 70 and/or its elements comprise any type orform of suitable hardware, programs, or a combination thereof tosupport, enable, or provide for the connection and use of multipledisplay devices 21. In one example, Computing Device 70 includes anytype or form of video adapter, video card, driver, and/or library tointerface, communicate, connect, or otherwise use display devices 21, Insome aspects, a video adapter may include multiple connectors tointerface to multiple display devices 21. In other aspects, ComputingDevice 70 includes multiple video adapters, with each video adapterconnected to one or more display devices 21. In some embodiments,Computing Device's 70 operating system can be configured for usingmultiple displays 21. In other embodiments, one or more display devices21 can be provided by one or more other computing devices such as remotecomputing devices connected to Computing Device 70 via a network.

In some embodiments, I/O device 13 can be a bridge between system bus 5and an external communication bus, such as a USB bus, an Apple DesktopBus, an RS-232 serial connection, a SCSI bus, a FireWire bus, a FireWire800 bus, an Ethernet bus, an AppleTalk bus, a Gigabit Ethernet bus, anAsynchronous Transfer Mode bus, a HIPPI bus, a Super HIPPI bus, aSerialPlus bus, a SCI/LAMP bus, a FibreChannel bus, a Serial Attachedsmall computer system interface bus, and/or other bus.

Computing Device 70 can operate under the control of an operating system17, which may support Computing Device's 70 basic functions, interfacewith and manage hardware resources, interface with and manageperipherals, provide common services for application programs, scheduletasks, and/or perform other functionalities. A modern operating systemenables features and functionalities such as a high resolution display,graphical user interface (GUI), touchscreen, cellular networkconnectivity (i.e. mobile operating system, etc.), Bluetoothconnectivity, WiFi connectivity, global positioning system (GPS)capabilities, mobile navigation, microphone, speaker, still picturecamera, video camera, voice recorder, speech recognition, music player,video player, near field communication, personal digital assistant(PDA), and/or other features, functionalities, or applications. Forexample, Computing Device 70 can use any conventional operating system,any embedded operating system, any real-time operating system, any opensource operating system, any video gaming operating system, anyproprietary operating system, any online operating system, any operatingsystem for mobile computing devices, or any other operating systemcapable of running on Computing Device 70 and performing operationsdescribed herein. Typical operating systems include: Windows XP, Windows7, Windows 8, etc. manufactured by Microsoft Corporation of Redmond,Wash.; Mac OS, iPhone OS, etc. manufactured by Apple Computer ofCupertino, Calif.; OS/2 manufactured by International Business Machinesof Armonk, N.Y.; Linux, a freely-available operating system distributedby Caldera Corp. of Salt Lake City, Utah; or any type or form of a Unixoperating system, among others. Any operating systems such as the onesfor Android devices can similarly be utilized.

Computing Device 70 can be implemented as or be part of variousdifferent model architectures such as web services, distributedcomputing, grid computing, cloud computing, and/or other architecturesor environments. For example, in addition to the traditional desktop,server, or mobile operating system architectures, a cloud-basedoperating system can be utilized to provide the structure on whichembodiments of the disclosure can be implemented. Other aspects ofComputing Device 70 can also be implemented in the cloud withoutdeparting from the spirit and scope of the disclosure. For example,memory, storage, processing, and/or other elements can be hosted in thecloud. In some embodiments, Computing Device 70 can be implemented onmultiple devices. For example, a portion of Computing Device 70 can beimplemented on a mobile device and another portion can be implemented onwearable electronics.

Computing Device 70 can be, or include, any mobile device, a mobilephone, a smartphone (i.e. iPhone, Windows phone, Blackberry, Androidphone, etc.), a tablet, a personal digital assistant (PDA), wearableelectronics, implantable electronics, or another mobile device capableof implementing the functionalities described herein. In otherembodiments, Computing Device 70 can be, or include, an embedded device,which can be any device or system with a dedicated function withinanother device or system. Embedded systems range from the simplest onesdedicated to one task with no user interface to complex ones withadvanced user interface that may resemble modern desktop computersystems. Examples of devices comprising an embedded device include amobile telephone, a personal digital assistant (PDA), a gaming device, amedia player, a digital still or video camera, a pager, a televisiondevice, a set-top box, a personal navigation device, a globalpositioning system (GPS) receiver, a portable storage device (i.e. a USBflash drive, etc.), a digital watch, a DVD player, a printer, amicrowave oven, a washing machine, a dishwasher, a gateway, a router, ahub, an automobile entertainment system, an automobile navigationsystem, a refrigerator, a washing machine, a factory automation device,an assembly line device, a factory floor monitoring device, athermostat, an automobile, a factory controller, a telephone, a networkbridge, and/or other devices. An embedded device can operate under thecontrol of an operating system for embedded devices such asMicroC/OS-II, QNX, VxWorks, eCos, TinyOS, Windows Embedded, EmbeddedLinux, and/or other embedded device operating systems.

Various implementations of the disclosed devices, systems, and/ormethods can be realized in digital electronic circuitry, integratedcircuitry, logic gates, specially designed application specificintegrated circuits (ASICs), field programmable gate arrays (FPGAs),computer hardware, firmware, programs, virtual machines, and/orcombinations thereof including their structural, logical, and/orphysical equivalents.

The disclosed devices, systems, and/or methods may include clients andservers. A client and server are generally remote from each other andtypically interact through a network. The relationship of a client andserver may arise by virtue of computer programs running on therespective computers and having a client-server relationship to eachother.

The disclosed devices, systems, and/or methods can be implemented in acomputing system that includes a back end component, a middlewarecomponent, a front end component, or any combination thereof. Thecomponents of the system can be interconnected by any form or medium ofdigital data communication such as, for example, a network.

Computing Device 70 may include or be interfaced with a computer programproduct comprising instructions or logic encoded on a computer-readablemedium that, when performed in a computing device, configure a processorto perform the operations and/or functionalities disclosed herein. Forexample, a computer program can be provided or encoded on acomputer-readable medium such as an optical medium (i.e. DVD-ROM, etc.),flash drive, hard drive, any memory, firmware, or other medium. Computerprogram can be installed onto a computing device to cause the computingdevice to perform the operations and/or functionalities disclosedherein. As used in this disclosure, machine-readable medium,computer-readable medium, or other such terms may refer to any computerprogram product, apparatus, and/or device for providing instructionsand/or data to a programmable processor. As such, machine-readablemedium includes any medium that can send or receive machine instructionsas a machine-readable signal. The term machine-readable signal may referto any signal used for providing instructions and/or data to aprogrammable processor. Examples of a machine-readable medium include avolatile and/or non-volatile medium, a removable and/or non-removablemedium, a communication medium, a storage medium, and/or other medium. Acommunication medium, for example, can transmit computer readableinstructions and/or data in a modulated data signal such as a carrierwave or other transport technique, and may include any other form ofinformation delivery medium known in art. A non-transitorymachine-readable medium comprises all machine-readable media except fora transitory, propagating signal.

Where a reference to a specific file or file type is used herein, otherfiles, file types, or formats can be substituted.

Where a reference to a data structure is used herein, it should beunderstood that any variety of data structures can be used such as, forexample, array, list, linked list, doubly linked list, queue, tree,heap, graph, map, grid, matrix, multi-dimensional matrix, table,database, database management system (DBMS), file, neural network,and/or any other type or form of a data structure including a customone. A data structure may include one or more fields or data fields thatare part of or associated with the data structure. A field or data fieldmay include a data, an object, a data structure, and/or any otherelement or a reference/pointer thereto. A data structure can be storedin one or more memories, files, or other repositories, A data structureand/or any elements thereof, when stored in a memory, file, or otherrepository, may be stored in a different arrangement than thearrangement of the data structure and/or any elements thereof. Forexample, a sequence of elements can be stored in an arrangement otherthan a sequence in a memory, file, or other repository.

Where a reference to a repository is used herein, it should beunderstood that a repository may be or include one or more files or filesystems, one or more storage locations or structures, one or morestorage systems, one or more data structures or objects, one or morememory locations or structures, and/or other storage, memory, or dataarrangements.

Where a reference to an interface is used herein, it should beunderstood that the interface comprises any hardware, device, system,program, method, and/or combination thereof that enable direct oroperative coupling, connection, and/or interaction of the elementsbetween which the interface is indicated. A line or arrow shown in thefigures between any of the depicted elements comprises such interface.Examples of an interface include a direct connection, an operativeconnection, a wired connection (i.e. wire, cable, etc.), a wirelessconnection, a device, a network, a bus, a circuit, a firmware, a driver,a bridge, a program, a combination thereof, and/or others.

Where a reference to an element coupled or connected to a processor isused herein, it should be understood that the element may be part of oroperating on the processor. Also, an element coupled or connected toanother element may include the element in communication or any otherinteractive relationship with the other element. Furthermore, an elementcoupled or connected to another element can be coupled or connected toany other element in alternate implementations. Terms coupled,connected, interfaced, or other such terms may be used interchangeablyherein.

Where a mention of a function, method, routine, subroutine, or othersuch procedure is used herein, it should be understood that thefunction, method, routine, subroutine, or other such procedure comprisesa call, reference, or pointer to the function, method, routine,subroutine, or other such procedure.

Where a mention of data, object, data structure, item, element, or thingis used herein, it should be understood that the data, object, datastructure, item, element, or thing comprises a reference or pointer tothe data, object, data structure, item, element, or thing.

The term match or matching can refer to total equivalence or similarity.

The term operating or operation can refer to processing, executing, orother such actions, and vice versa. Therefore, the terms operating,operation, processing, executing, or other such actions may be usedinterchangeably herein.

The term collection of elements can refer to plurality of elementswithout implying that the collection is an element itself.

Referring to FIG. 2A, an embodiment of utilizing System for LearningAIIMs 100 in a dedicated device is illustrated. In some aspects, thedevice can be placed on a table between Conversation Participants 50 aand 50 b as shown. In other aspects, the device can be mounted,attached, or placed on a wall, ceiling, or other convenient object orlocation.

Referring to FIG. 2B, an embodiment of internal structure of System forLearning AIIMs 100 in a dedicated device is illustrated. System forLearning AIIMs 100 comprises interconnected Knowledge Structuring Unit110, Knowledgebase 130, Picture-capturing Devices 140 a and 140 b,Sound-capturing Devices 150 a and 150 b, and Activity Detectors 160 aand 160 b. Some embodiments of Activity Detector 160 a may includePicture Recognizer 163 a and Speech/Sound Recognizer 165 a whereas someembodiments of Activity Detector 160 b may include Picture Recognizer163 b and Speech/Sound Recognizer 165 b. Other additional elements canbe included as needed, or some of the disclosed ones can be excluded, ora combination thereof can be utilized in alternate embodiments.

System for Learning AIIMs 100 comprises any hardware, programs, or acombination thereof. System for Learning AIIMs 100 comprises thefunctionality for learning conversations. As the participants in aconversation exchange verbal and visual expressions or communication,System for Learning AIIMs 100 may capture and learn these conversationalexchanges. Additionally, System for Learning AIIMs 100 may interconnect,interrelate, or interlink rounds of conversational exchange into aknowledge structure such as Knowledgebase 130, Neural Network 130 a,Graph 130 b, Collection of Sequences 130 c, Sequence 133, and/or otherdata or knowledge structure. System for Learning AIIMs 100 alsocomprises the functionality for learning a person's conversational styleor character. Knowledge learned by System for Learning AIIMs 100 can beused to simulate a conversation with a person (i.e. AI ConversationParticipant 55 [later described], etc) in the person's absence, afterthe person is deceased, or in any situation where a conversation may beneeded with the person. For example, knowledge learned by System forLearning AIIMs 100 can be used by a System for Using AIIMs 500 (laterdescribed) to simulate a conversation with a parent, predecessor (i.e.grandparent, etc.), spouse, friend, historical figure, famous person(i.e. scientist, athlete, actor, musician, politician, etc.), and/orother persons. System for Learning AIIMs 100 comprises otherfunctionalities disclosed herein. Statistical, artificial intelligence,machine learning, and/or other models or techniques are utilized toimplement the disclosed devices, systems, and methods.

In some embodiments, the disclosed systems for learning and using AIIMs(i.e. System for Learning AIIMs 100, System for Using AIIMs 500 [laterdescribed], etc.), or elements thereof, can be implemented entirely orin part in a device (i.e. microchip, circuitry, logic gates, electronicdevice, computing device, special or general purpose processor, etc.) orsystem that comprises (i.e. hard coded, internally stored, etc.) or isprovided with (i.e. externally stored, etc.) instructions forimplementing AIIM functionalities. As such, the disclosed systems forlearning and using AIIMs, or elements thereof, may include theprocessing, memory, storage, and/or other features, functionalities, andembodiments of Processor 11 and/or other elements of Computing Device70. Such device or system can operate on its own (i.e. standalonedevice, etc.), be embedded in another device or system (i.e. atelevision device, a set-top box, a gaming device, a smartphone, a stillor motion picture camera, and/or any other device capable of housing theelements needed for AIIM functionalities), work in combination withother devices or systems, or be available in any other configuration. Inother embodiments, the disclosed systems for learning and using AIIMs,or elements thereof, can be implemented entirely or in part as acomputer program and executed by one or more Processors 11. Such system,or elements thereof, can be implemented in one or more modules or unitsof a single or multiple computer programs. In yet other embodiments, thedisclosed systems for learning and using AIIMs, or elements thereof, maybe included in Alternative Memory 16 that provides instructions forimplementing AIIM functionalities to one or more Processors 11. Infurther embodiments, the disclosed systems for learning and using AIIMs,or elements thereof, can be implemented as a network, web, distributed,cloud, or other such application accessed on one or more remotecomputing devices (i.e. servers, cloud, etc.) via Network Interface 25,such remote computing devices including processing capabilities andinstructions for implementing AIIM functionalities. In some aspects, thedisclosed systems for learning and using AIIMs, or elements thereof, canbe attached to or interfaced with any computing device or applicationprogram, included as a feature of an operating system running on acomputing device, built (i.e. hard coded, etc.) into any computingdevice or application program, and/or available in any otherconfiguration to provide its functionalities.

In one example, the teaching presented by the disclosure can beimplemented in a device or system for learning AIIMs. The device orsystem may include one or more processor circuits coupled to a memoryunit. The device or system may further include a first picture-capturingdevice configured to capture a stream of digital pictures of a firstconversation participant and a second picture-capturing deviceconfigured to capture a stream of digital pictures of a secondconversation participant, the first and the second picture-capturingdevices coupled to the one or more processor circuits. The device orsystem may further include a first sound-capturing device configured tocapture a stream of digital sound samples of the first conversationparticipant and a second sound-capturing device configured to capture astream of digital sound samples of the second conversation participant,the first and the second sound-capturing devices coupled to the one ormore processor circuits. The one or more processor circuits may beconfigured to detect the first conversation participant's firstconversational activity from at least one of the stream of digitalpictures of the first conversation participant or the stream of digitalsound samples of the first conversation participant, and detect thesecond conversation participant's first conversational activity from atleast one of the stream of digital pictures of the second conversationparticipant or the stream of digital sound samples of the secondconversation participant. The one or more processor circuits may also beconfigured to generate a first round of conversational exchangeincluding a recording of the first conversation participant's firstconversational activity and a recording of the second conversationparticipant's first conversational activity. The one or more processorcircuits may also be configured to cause the memory unit to store thefirst round of conversational exchange, the first round ofconversational exchange being part of a stored plurality of rounds ofconversational exchange. Any of the operations of the described elementscan be performed repeatedly and/or in different orders in alternateembodiments. In some aspects, the one or more processor circuits, thememory unit, the first picture-capturing device, the secondpicture-capturing device, the first sound-capturing device, and thesecond sound-capturing device are part of a single device. In otheraspects, at least one of: the one or more processor circuits or thememory unit are part of a server, whereas, the first picture-capturingdevice and the first sound-capturing device are part of a firstcomputing device, and the second picture-capturing device and the secondsound-capturing device are part of a second computing device, the firstand the second computing devices coupled to the server via a network.Other additional elements can be included as needed, or some of thedisclosed ones can be excluded, or a combination thereof can be utilizedin alternate embodiments. The device or system for learning AIIMs mayinclude any actions or operations of any of the disclosed methods suchas methods 6100 and/or 6200 (all later described).

In another example, any of the systems disclosed herein (i.e. System forLearning AIIMs 100, System for Using AIIMs 500 [later described], etc.),or elements thereof, can be implemented at least in part as a computerprogram such as a Java application or program. Java provides a robustand flexible environment for application programs including flexibleuser interfaces, robust security, built-in network protocols, powerfulapplication programming interfaces, database or DBMS connectivity andinterfacing functionalities, file manipulation capabilities, support fornetworked applications, and/or other features or functionalities.Application programs based on Java can be portable across many devices,yet leverage each device's native capabilities. Java supports thefeature sets of most smartphones and a broad range of connected deviceswhile still fitting within their resource constraints. Various Javaplatforms include virtual machine features comprising a runtimeenvironment for application programs such as some embodiments of thesystems disclosed herein (i.e. System for Learning AIIMs 100, System forUsing AIIMs 500, etc.), or elements thereof. Java platforms provide awide range of user-level functionalities that can be implemented inapplication programs such as an Internet browser, displaying text andgraphics, playing and recording audio content, displaying and recordingvisual content, communicating with another computing device, and/orother functionalities. It should be understood that the systemsdisclosed herein (i.e. System for Learning AIIMs 100, System for UsingAIIMs 500, etc.), or elements thereof, are programming language,platform, and operating system independent, Examples of programminglanguages that can be used instead of or in addition to Java include C,C++, Cobol, Python, Java Script, Tcl, Visual Basic, Pascal, VB Script,Perl, PHP, Ruby, and/or other programming languages capable ofimplementing the functionalities described herein.

Knowledgebase 130 comprises the functionality for storing the knowledgeof one or more conversations, and/or other functionalities. In someembodiments, Knowledgebase 130 may be or include Neural Network 130 a(later described), In other embodiments, Knowledgebase 130 may be orinclude Graph 130 b (later described). In further embodiments,Knowledgebase 130 may be or include Collection of Sequences 130 c (laterdescribed). In further embodiments, Knowledgebase 130 may be or includeSequence 133 (later described). In general, Knowledgebase 130 may be orinclude any knowledge or data structure capable of storing the knowledgeof one or more conversations and/or other data. Knowledgebase 130 mayreside locally on System for Learning AIIMs 100 or Computing Device 70,or remotely (i.e. remote Knowledgebase 130, etc.) on a remote computingdevice (i.e. server, cloud, etc.) accessible over a network.

Picture-capturing Device 140 comprises the functionality for capturingone or more pictures, and/or other functionalities. As such,Picture-capturing Device 140 can be used to capture pictures comprisinga Conversation Participant's 50 visual expressions or communication.Most modern computing and mobile devices include Picture-capturingDevice 140 as one of the input devices. In some embodiments,Picture-capturing Device 140 may be or comprises a motion or stillpicture camera or other picture capturing device. In general,Picture-capturing Device 140 may capture any light (i.e. visible light,infrared light, ultraviolet light, x-ray light, etc.) across theelectromagnetic spectrum onto a light-sensitive material. In oneexample, a digital Picture-capturing Device 140 can utilize a chargecoupled device (CCD), a CMOS sensor, and/or other electronic imagesensor to capture a Stream of Digital Pictures 143 (later described)that can then be stored in a memory, storage, or transmitted to aprocessing element such as Activity Detector 160, Knowledge StructuringUnit 110, and/or other disclosed elements. In another example, analogPicture-capturing Device 140 can utilize an analog-to-digital converterto produce a Stream of Digital Pictures 143. In some embodiments,Picture-capturing Device 140 can be built, embedded, or integrated inSystem for Learning AIIMs 100. In other embodiments, Picture-capturingDevice 140 can be an external Picture-capturing Device 140 connectedwith System for Learning AIIMs 100. In further embodiments,Picture-capturing Device 140 comprises Computing Device 70 or elementsthereof. In general, Picture-capturing Device 140 can be implemented inany suitable configuration to provide its functionalities.

Sound-capturing Device 150 comprises the functionality for capturing oneor more sounds, and/or other functionalities. As such, Sound-capturingDevice 150 can be used to capture sounds comprising a ConversationParticipant's 50 verbal expressions or communication. Most moderncomputing and mobile devices include Sound-capturing Device 150 as oneof the input devices. In some embodiments, Sound-capturing Device 150may be or comprises a microphone or other sound capturing device. Insome designs, microphone may be a directional microphone that enablescapturing sounds from a direction where it is pointed while ignoring orbeing insensitive to sounds from other directions. Such directionalmicrophone may be used for capturing sounds of a particular ConversationParticipant 50 while ignoring or being insensitive to other sounds (i.e.ambient sounds, noise, sounds of other Conversation Participants 50,etc.). In general, Sound-capturing Device 150 may produce electricalsignal from air pressure variations. Samples of the electrical signalcan then be read to produce a stream of digital sound samples usedherein. In one example, a digital Sound-capturing Device 150 may includean integrated analog-to-digital converter to capture a Stream of DigitalSound Samples 153 that can then be stored in a memory, storage, ortransmitted to a processing element such as Activity Detector 160,Knowledge Structuring Unit 110, and/or other disclosed elements. Inanother example, analog Sound-capturing Device 150 may utilize anexternal analog-to-digital converter to produce a Stream of DigitalSound Samples 153. In some embodiments, Sound-capturing Device 150 canbe built, embedded, or integrated in System for Learning AIIMs 100. Inother embodiments, Sound-capturing Device 150 can be an externalSound-capturing Device 150 connected with System for Learning AIIMs 100.In further embodiments, Sound-capturing Device 150 comprises ComputingDevice 70 or elements thereof. In general, Sound-capturing Device 150can be implemented in any suitable configuration to provide itsfunctionalities.

Stream of Digital Pictures 143 comprises the functionality for storing aplurality of digital pictures, and/or other functionalities. In someaspects, Stream of Digital Pictures 143 comprises a live feed fromPicture-capturing Device 140. In other aspects, Stream of DigitalPictures 143 comprises previously captured and stored Stream of DigitalPictures 143. System for Learning AIIMs 100 can use the previouslycaptured and stored Streams of Digital Pictures 143 to implement itslearning functionalities in which case Picture-capturing Device 140 canoptionally be omitted. Pictures (i.e. frames) in a stream of digitalpictures can be time stamped or sequenced. In some embodiments, Streamof Digital Pictures 143 comprises any type or form of digital motionpicture such as MPEG, AVI, FLV, MOV, RM, SWF, WMV, DivX, and/or otherdigitally encoded motion picture. In other embodiments, Stream ofDigital Pictures 143 comprises a plurality of any type or form ofdigital pictures such as digital bitmaps, JPEG pictures, GIF pictures,TIFF pictures, PDF pictures, and/or others. In yet other embodiments,Stream of Digital Pictures 143 comprises any computer-generated picturessuch as views of a 3D game, 3D application, or CAD/CAM applicationcaptured or rendered as a stream of digital pictures. In furtherembodiments, Stream of Digital Pictures 143 comprises any application orprocess that can generate a stream of digital pictures or other visualcontent. A Stream of Digital Pictures 143 and a Stream of Digital SoundSamples 153 may commonly be included in a file (i.e. video file, etc.)comprising both the Stream of Digital Pictures 143 and the Stream ofDigital Sound Samples 153. Stream of Digital Pictures 143 includes anyfeatures, functionalities, and embodiments of Sub-stream of DigitalPictures 145 (later described), and vice versa. Also, any operations onStream of Digital Pictures 143 can be similarly performed on Sub-streamof Digital Pictures 145, and vice versa.

Stream of Digital Sound Samples 153 comprises the functionality forstoring a plurality of digital sound samples, and/or otherfunctionalities. In some aspects, Stream of Digital Sound Samples 153comprises a live feed from Sound-capturing Device 150. In other aspects,Stream of Digital Sound Samples 153 comprises previously captured andstored Stream of Digital Sound Samples 153. System for Learning AIIMs100 can use the previously captured and stored Streams of Digital SoundSamples 153 to implement its learning functionalities in which caseSound-capturing Device 150 can optionally be omitted. Sound samples in astream of digital sound samples can be time stamped or sequenced. Insome embodiments, Stream of Digital Sound Samples 153 comprises any typeor form of digital sound such as WAV, WMA, AIFF, MP3, RA, OGG, and/orother digitally encoded sound. In other embodiments, Stream of DigitalSound Samples 153 comprises any computer-generated stream of digitalsound samples such as synthesized sound. In further embodiments, Streamof Digital Sound Samples 153 comprises any application or process thatcan generate a stream of digital sound samples or other audio content.Stream of Digital Sound Samples 153 includes any features,functionalities, and embodiments of Sub-stream of Digital Sound Samples155 (later described), and vice versa. Also, any operations on Stream ofDigital Sound Samples 153 can be similarly performed on Sub-stream ofDigital Sound Samples 155, and vice versa.

In some embodiments, Picture-capturing Device 140 a may capture Streamof Digital Pictures 143 a comprising Conversation Participant's 50 avisual expressions or communication. Sound-capturing Device 150 a maycapture Stream of Digital Sound Samples 153 a comprising ConversationParticipant's 50 a verbal expressions or communication. In some aspects,Stream of Digital Pictures 143 a is captured simultaneously with Streamof Digital Sound Samples 153 a. Stream of Digital Pictures 143 a maytherefore be associated with or correspond to Stream of Digital SoundSamples 153 a. Similarly, Picture-capturing Device 140 b may captureStream of Digital Pictures 143 b comprising Conversation Participant's50 b visual expressions or communication. Sound-capturing Device 150 bmay capture Stream of Digital Sound Samples 153 b comprisingConversation Participant's 50 b verbal expressions or communication. Insome aspects, Stream of Digital Pictures 143 b is capturedsimultaneously with Stream of Digital Sound Samples 153 b. Stream ofDigital Pictures 143 b may therefore be associated with or correspond toStream of Digital Sound Samples 153 b.

Activity Detector 160 comprises the functionality for identifying,detecting, or determining conversational activities (also referred tosimply as activities, etc.) of a Conversation Participant 50, and/orother functionalities. Examples of conversational activities includespeaking, silent facial expressions, silent body movements, motionlesssilence, absence from the conversation, and/or others. Silent facialexpressions, silent body movements, motionless silence, and/or othersilent activities may be referred to as observing conversationalactivities. Some aspects of a conversation may involve exchanging verbalcommunication (i.e. speech, sounds, etc.) among ConversationParticipants 50. Other aspects of a conversation may involve exchangingvisual communication (i.e. facial expressions, gestures, body language,etc.) among Conversation Participants 50. These and/or other forms ofcommunication or expressions may also be combined. Activity Detector 160can detect conversational activities of a Conversation Participant 50 byprocessing either or both Stream of Digital Pictures 143 comprising theConversation Participant's 50 visual expressions or communication and/orStream of Digital Sound Samples 153 comprising the ConversationParticipant's 50 verbal expressions or communication. Activity Detector160 comprises the functionality for identifying Sub-streams of DigitalPictures 145 in a Stream of Digital Pictures 143. A Sub-stream ofDigital Pictures 145 may include a Conversation Participant's 50 visualexpressions or communication in a part of a conversation. Similarly,Activity Detector 160 comprises the functionality for identifyingSub-streams of Digital Sound Samples 155 in a Stream of Digital SoundSamples 153. A Sub-stream of Digital Sound Samples 155 may include aConversation Participant's 50 verbal expressions or communication in apart of a conversation. Activity Detector 160 comprises thefunctionality for creating or generating a Conversational Activity 210(also referred to as activity, Cony Activity, etc.) and storing one ormore Sub-streams of Digital Pictures 145, one or more Sub-streams ofDigital Sound Samples 155, and/or other data (i.e. Extra info 250 [laterdescribed], etc.) into the Conversational Activity 210. As such,Conversational Activity 210 comprises the functionality for storing oneor more Sub-streams of Digital Pictures 145, one or more Sub-streams ofDigital Sound Samples 155, and/or other data. Conversational Activity210 may therefore include a recording or data structure of aConversation Participant's 50 conversational activity. Activity Detector160 also comprises the functionality for extracting or filtering personsand/or objects of interest from Sub-streams of Digital Pictures 145, andextracting or filtering speech and/or sounds of interest fromSub-streams of Digital Sound Samples 155 as later described.

In one example, Activity Detector 160 a can detect ConversationParticipant's 50 a speaking activity by recognizing ConversationParticipant's 50 a speech in Stream of Digital Sound Samples 153 a.Specifically, for instance, Activity Detector 160 a can identify abeginning of Conversation Participant's 50 a speaking activity byrecognizing Conversation Participant's 50 a speech in Stream of DigitalSound Samples 153 a after a threshold period of silence (i.e. no speechor sound, etc.). Further, Activity Detector 160 can identify an end ofConversation Participant's 50 a speaking activity by recognizing athreshold period of silence in Stream of Digital Sound Samples 153 aafter the Conversation Participant's 50 a speech. Recognizing silence inStream of Digital Sound Samples 153 a may mark a beginning of a newactivity such as silent facial expressions, silent body movements,motionless silence, absence from the conversation, and/or otheractivity. In another example, Activity Detector 160 a can detectConversation Participant's 50 a silent facial expressions activity byrecognizing Conversation Participant's 50 a facial expressions (i.e.smiling, lifting eyebrows, etc.) in Stream of Digital Pictures 143 a andby recognizing Conversation Participant's 50 a silence (i.e. no speechor sound, etc.) in Stream of Digital Sound Samples 153 a. In anotherexample, Activity Detector 160 a can detect Conversation Participant's50 a silent body movements activity by recognizing ConversationParticipant's 50 a body movements (i.e. nodding head, shaking head,shrugging shoulders, pointing finger, pointing fist, etc.) in Stream ofDigital Pictures 143 a and by recognizing Conversation Participant's 50a silence (i.e. no speech or sound, etc.) in Stream of Digital SoundSamples 153 a. In a further example, Activity Detector 160 a can detectConversation Participant's 50 a motionless silence activity byrecognizing no or marginal motion (i.e. no facial change, no bodymovement, etc.) of Conversation Participant 50 a in Stream of DigitalPictures 143 a and by recognizing Conversation Participant's 50 asilence (i.e. no speech or sound, etc.) in Stream of Digital SoundSamples 153 a. For instance, marginal motion of Conversation Participant50 a may include comparing one picture of Stream of Digital Pictures 143a with another (i.e. subsequent, etc.) picture of Stream of DigitalPictures 143 a and determining that a number or percentage differencebetween regions of the two pictures comprising Conversation Participant50 a does not exceed a threshold. Other techniques known in art fordetermining marginal motion can be utilized, in a further example,Activity Detector 160 can detect Conversation Participant's 50 a absencefrom the conversation activity by recognizing Conversation Participant's50 a absence (i.e. Conversation Participant 50 a missing from the fieldof view, etc.) in Stream of Digital Pictures 143 a and/or by recognizingConversation Participant's 50 a silence (i.e. no speech or sound, etc.)in Stream of Digital Sound Samples 153 a. In some aspects, detecting anynew conversational activity may mark an end to a previously detectedconversational activity. In other aspects, Conversation Participant's 50b conversational activity may affect the determination of ConversationParticipant's 50 a conversational activity (i.e. one conversationparticipant's conversational activity may be related or depend onanother conversation participant's conversational activity, etc.).Activity Detector 160 may identify Sub-stream of Digital Pictures 145 ain Stream of Digital Pictures 143 a, the Sub-stream of Digital Pictures145 a comprising Conversation Participant's 50 a visual expressions orcommunication in a part of a conversation. Similarly, Activity Detector160 may identify Sub-stream of Digital Sound Samples 155 a in Stream ofDigital Sound Samples 153 a, the Sub-stream of Digital Sound Samples 155a comprising Conversation Participant's 50 a verbal expressions orcommunication in a part of a conversation. Activity Detector 160 maythen create or generate Conversational Activity 210 comprisingSub-stream of Digital Pictures 145 a, Sub-stream of Digital SoundSamples 155 a, and/or other data (i.e. Extra Info 250 [later described],etc.). Activity Detector 160 b may detect Conversation Participant's 50b activities, and create or generate Conversation Participant's 50 bConversational Activities 210 using similar techniques as describedabove.

In some embodiments, Activity Detector 160 can utilize PictureRecognizer 163 to detect persons, objects, and/or their activities inStream of Digital Pictures 143. Similarly, Activity Detector 160 canutilize Speech/Sound Recognizer 165 to detect speech and/or sounds inStream of Digital Sound Samples 153. In general, Activity Detector 160and/or other disclosed elements can use Picture Recognizer 163 and/orSpeech/Sound Recognizer 165 for any other operation supported by PictureRecognizer 163 and/or Speech/Sound Recognizer 165.

Picture Recognizer 163 comprises the functionality for detecting orrecognizing persons or objects in visual data. Picture Recognizer 163comprises the functionality for detecting activities in visual data.Picture Recognizer 163 comprises the functionality for tracking persons,objects and/or their activities in visual data. Picture Recognizer 163comprises other disclosed functionalities. Visual data includes digitalmotion pictures, digital still pictures (i.e. bitmaps, etc.), and/orother visual data, Examples of file formats that can be utilized tostore visual data include AVI, DivX, MPEG, REG, GIF, TIFF, PNG, PDF,and/or other file formats. Picture Recognizer 163 may detect orrecognize a person and/or his/her activities as well as track the personand/or his/her activities in Stream of Digital Pictures 143. PictureRecognizer 163 may detect or recognize a human head or face, upper body,full body, or portions/combinations thereof. In some aspects, PictureRecognizer 163 may detect or recognize persons, objects, and/or theiractivities from a picture (i.e. frame, etc.) of Stream of DigitalPictures 143 by comparing regions of pixels from the picture (i.e.frame, etc.) with collections of pixels comprising known persons,objects, and/or their activities. The collections of pixels comprisingknown persons, objects, and/or their activities can be learned, ormanually, programmatically, or otherwise defined. The collections ofpixels comprising known persons, objects, and/or their activities can bestored in any data structure or repository (i.e. database, etc.) thatresides locally on System for Learning AIIMs 100 or Computing Device 70,or remotely on a remote computing device (i.e. server, cloud, etc.)accessible over a network. In other aspects, Picture Recognizer 163 maydetect or recognize persons, objects, and/or their activities from apicture (i.e. frame, etc.) of Stream of Digital Pictures 143 bycomparing features (i.e. lines, edges, ridges, corners, blobs, regions,etc.) of the picture (i.e. frame, etc.) with features of known persons,objects, and/or their activities. The features of known persons,objects, and/or their activities can be learned, or manually,programmatically, or otherwise defined. The features of known persons,objects, and/or their activities can be stored in any data structure orrepository (i.e. neural network, database, etc.) that resides locally onSystem for Learning AIIMs 100 or Computing Device 70, or remotely on aremote computing device (i.e. server, cloud, etc.) accessible over anetwork, Typical steps or elements in a feature oriented picturerecognition include pre-processing, feature extraction,detection/segmentation, decision-making, and/or others, or a combinationthereof, each of which may include its own sub-steps or sub-elementsdepending on the application. In further aspects, Picture Recognizer 163may detect or recognize multiple persons, objects, and/or theiractivities from a picture (i.e. frame, etc.) of Stream of DigitalPictures 143 using the aforementioned pixel or feature comparisons,and/or other detection or recognition techniques. For example, a pictureof Stream of Digital Pictures 143 may depict two persons in two of itsregions both of whom Picture Recognizer 163 can detect simultaneously.This functionality can be used in embodiments where ConversationParticipants 50 need to be detected or recognized in a single Stream ofDigital Pictures 143. In further aspects, where persons, objects, and/ortheir activities span multiple pictures, Picture Recognizer 163 maydetect or recognize persons, objects, and/or their activities byapplying the aforementioned pixel or feature comparisons and/or otherdetection or recognition techniques over a stream or sub-stream ofpictures. For example, once a person is detected in a picture of Streamof Digital Pictures 143, the region of pixels comprising the detectedperson or the person's features can be searched in other pictures ofStream of Digital Pictures 143, thereby tracking the person through theStream of Digital Pictures 143. In further aspects, Picture Recognizer163 may detect or recognize a person's activities by identifying and/oranalyzing differences between a detected region of pixels of one pictureand detected regions of pixels of other pictures in Stream of DigitalPictures 143. For example, a region of pixels comprising a person's facecan be detected in multiple consecutive pictures of Stream of DigitalPictures 143. Differences among the detected regions of the consecutivepictures may be identified in the mouth part of the person's face, anddetermined to be continuous over a time period (i.e. as opposed to asingle random mouth movement, etc.) and diverse in variety (i.e. asopposed to a smile, etc.) to indicate a speaking activity. Any techniquefor recognizing speech from mouth/lip movements can be used in this andother examples. In further aspects, Picture Recognizer 163 may detect orrecognize persons, objects, and/or their activities using one or moreartificial neural networks, which may include statistical techniques,Examples of artificial neural networks that can be used in PictureRecognizer 163 include convolutional neural networks (CNNs), time delayneural networks (TDNNs), deep neural networks, and/or others. In oneexample, picture recognition techniques and/or tools involvingconvolutional neural networks may include identifying and/or analyzingtiled and/or overlapping regions or features of a picture, which maythen be used to search for pictures with matching regions or features.In another example, features of different convolutional neural networksresponsible for spatial and temporal streams can be fused to detectpersons, objects, and/or their activities in motion pictures. Ingeneral, Picture Recognizer 163 may include any machine learning, deeplearning, and/or other artificial intelligence techniques. Any othertechniques known in art can be utilized in Picture Recognizer 163. For,example, thresholds for similarity, statistical, and/or optimizationtechniques can be utilized to determine a match in any of theabove-described detection or recognition techniques, Picture Recognizer163 comprises any features, functionalities, and embodiments ofSubstantial Similarity Comparison 125 (later described).

In some exemplary embodiments, facial recognition techniques and/ortools such as OpenCV (Open Source Computer Vision) library, AnimetricsFaceR API, Lambda Labs Facial Recognition API, Face++ SDK, Neven Vision(also known as N-Vision) Engine, and/or others can be utilized fordetecting or recognizing faces in digital pictures. In some aspects,facial recognition techniques and/or tools involve identifying and/oranalyzing facial features such as the relative position, size, and/orshape of the eyes, nose, cheekbones, jaw, etc., which may then be usedto search for pictures with matching features. For example, FaceR APIcan detect a person's face in Stream of Digital Pictures 143 captured byPicture-capturing Device 140 or stored in an electronic repository,which can then be utilized in Activity Detector 160, KnowledgeStructuring Unit 110, and/or other elements.

In other exemplary embodiments, object recognition techniques and/ortools such as OpenCV (Open Source Computer Vision) library, CamFind API,Kooaba, 6px API, Dextro API, and/or others can be utilized for detectingor recognizing objects (i.e. objects, animals, people, etc.) in digitalpictures. In some aspects, object recognition techniques and/or toolsinvolve identifying and/or analyzing object features such as lines,edges, ridges, corners, blobs, regions, and/or their relative positions,sizes, shapes, etc., which may then be used to search for pictures withmatching features. For example, OpenCV library can detect an object(i.e. car, pedestrian, door, building, animal, person, etc.) in Streamof Digital Pictures 143 captured by Picture-capturing Device 140 orstored in an electronic repository, which can then be utilized inActivity Detector 160, Knowledge Structuring Unit 110, and/or otherelements.

Speech/Sound Recognizer 165 comprises the functionality for detecting orrecognizing speech or sounds in audio data. Speech/Sound Recognizer 165comprises other disclosed functionalities, Audio data includes digitalsound, and/or other audio data. Examples of file formats that can beutilized to store audio data include WAV, WMA, AIFF, MP3, RA, OGG,and/or other file formats. Speech/Sound Recognizer 165 may detect orrecognize speech or sounds of a person in Stream of Digital SoundSamples 153. In some aspects, Speech/Sound Recognizer 165 may detect orrecognize a person's speech or sounds from Stream of Digital SoundSamples 153 by comparing collections of sound samples from the Stream ofDigital Sound Samples 153 with collections of known sound samples. Thecollections of known sound samples can be learned, or manually,programmatically, or otherwise defined. The collections of known soundsamples can be stored in any data structure or repository (i.e.database, etc.) that resides locally on System for Learning AIIMs 100 orComputing Device 70, or remotely on a remote computing device (i.e.server, cloud, etc.) accessible over a network. In other aspects,Speech/Sound Recognizer 165 may detect or recognize a person's speech orsounds from Stream of Digital Sound Samples 153 by comparing featuresfrom the Stream of Digital Sound Samples 153 with features of knownsounds. The features of known sounds can be learned, or manually,programmatically, or otherwise defined. The features of known sounds canbe stored in any data structure or repository (i.e. database, neuralnetwork, etc.) that resides locally on System for Learning AIIMs 100 orComputing Device 70, or remotely on a remote computing device (i.e.server, cloud, etc.) accessible over a network. Typical steps orelements in a feature oriented speech or sound recognition includepre-processing, feature extraction, acoustic modeling, languagemodeling, and/or others, or a combination thereof, each of which mayinclude its own sub-steps or sub-elements depending on the application.Either or both acoustic modeling and/or language modeling can be used inspeech or sound recognition. Acoustic features can be used forclassification of non-verbal vocal outbursts such as laughter or sighswhereas linguistic features can be used to transcribe the linguisticmessage such as words, phrases, or sentences. In further aspects,Speech/Sound Recognizer 165 may be implemented as speaker dependent orspeaker independent, Speaker dependent speech or sound recognition mayidentify a speaker in addition to recognizing his/her speech. In furtheraspects, Speech/Sound Recognizer 165 may detect or recognize speeches orsounds of multiple persons from Stream of Digital Sound Samples 153using the aforementioned sound sample or feature comparisons, usingspeaker dependent speech/sound recognition, and/or using other detectionor recognition techniques. In one example, speaker dependentspeech/sound recognition may involve analyzing characteristics of aperson's voice or speech, thereby distinguishing it from other persons'voices or speeches. A person's voice or speech characteristics neededfor speaker dependent speech recognition can be learned automaticallythrough the learning of conversations disclosed herein. For instance,such automatic learning of a person's voice or speech characteristicscan be accomplished in situations where the person's voice is completelyor relatively isolated from other persons' voices (i.e. whereconversation participants are remote from one another, invideoconferencing, etc.). In another example, a person's voice or speechcharacteristics needed for speaker dependent speech recognition can belearned through a training session where a person may read text orisolated vocabulary and the system may analyze the person's voice orspeech characteristics to fine-tune the recognition of that person'sspeech. Speaker dependent speech recognition functionality can be usedin embodiments where speeches of multiple Conversation Participants 50need to be detected or recognized in a single Stream of Digital SoundSamples 153. Speaker dependent speech recognition functionality can alsobe used in cases where Conversation Participants 50 are in closeproximity to one another (i.e. in the same room, etc.) and where all oftheir voices can be detected by one or more Sound-capturing Devices 150.In further aspects, Speech/Sound Recognizer 165 may detect or recognizea variety of sounds from Stream of Digital Sound Samples 153 using theaforementioned sound sample or feature comparisons and/or otherdetection or recognition techniques. For example, sound of wind, doorclosing, car passing, and/or other sounds can be detected and used asextra or contextual information (i.e. Extra Info 250 [later described],etc.) in matching, decision-making, and/or other elements orfunctionalities herein. In further aspects, Speech/Sound Recognizer 165may be implemented as keyword spotting or as full speech recognition.Keyword spotting may attempt to find only a select group of words and/orphrases, and because of this limited lexicon consumes fewer resources.Full speech recognition may attempt to find all the words and/orphrases, and because of this broader lexicon consumes significantresources. In further aspects, Speech/Sound Recognizer 165 may detect orrecognize speech or sounds using Hidden Markov Models (HMM), ArtificialNeural Networks, Dynamic Time Warping (DTW), Gaussian Mixture Models(GMM), and/or other models or techniques, or a combination thereof. Someor all of these models or techniques may include statistical techniques.Examples of artificial neural networks that can be used in Speech/SoundRecognizer 165 include recurrent neural networks, time delay neuralnetworks (TDNNs), deep neural networks, and/or others. In general,Speech/Sound Recognizer 165 may include any machine learning, deeplearning, and/or other artificial intelligence techniques. Any othertechniques known in art can be utilized in Speech/Sound Recognizer 165.For example, thresholds for similarity, statistical, and/or optimizationtechniques can be utilized to determine a match in any of theabove-described detection or recognition techniques. Speech/SoundRecognizer 165 comprises any features, functionalities, and embodimentsof Substantial Similarity Comparison 125 (later described).

In some exemplary embodiments, operating system's speech/soundrecognition functionalities such as iOS's Voice Services, Siri, and/orothers can be utilized in Speech/Sound Recognizer 165. For example, iOSVoice Services can detect speech/sound in Stream of Digital SoundSamples 153 captured by Sound-capturing Device 150 or stored in anelectronic repository, which can then be utilized in Activity Detector160, Knowledge Structuring Unit 110, and/or other elements.

In other exemplary embodiments, Java Speech API (JSAPI) implementationsuch as The Cloud Garden, Sphinx, and/or others can be utilized inSpeech/Sound Recognizer 165. For example, Cloud Garden JSAPI can detectspeech/sound in Stream of Digital Sound Samples 153 captured bySound-capturing Device 150 or stored in an electronic repository, whichcan then be utilized in Activity Detector 160, Knowledge StructuringUnit 110, and/or other elements. Any other programming language's orplatform's speech or sound processing API can similarly be utilized.

In further exemplary embodiments, applications or engines providingspeech/sound recognition functionalities such as HTK (Hidden MarkovModel Toolkit), Kaldi, OpenEars, Dragon Mobile, Julius, iSpeech,CeedVocal, and/or others can be utilized in Speech/Sound Recognizer 165.For example, Kaldi SDK can detect speech/sound in Stream of DigitalSound Samples 153 captured by Sound-capturing Device 150 or stored in anelectronic repository, which can then be utilized in Activity Detector160, Knowledge Structuring Unit 110, and/or other elements.

Referring to FIG. 3A, an embodiment of Activity Detector 160 extractingor filtering persons and/or objects of interest is illustrated. Thisway, learning of conversations can focus on Conversation Participants'50 visual expressions or communication regardless of and acrossdifferent visual backgrounds, surrounding objects, and/or otherinsignificant content. In one example, a picture of Sub-stream ofDigital Pictures 145 may include Conversation Participant 50 andbackground objects such as clouds, sky, and/or other objects (i.e.trees, buildings, vehicles, etc.) as shown. Activity Detector 160 candetect Conversation Participant's 50 face using Picture Recognizer 163(i.e. facial recognition, etc.) and/or other techniques. Once detected,Activity Detector 160 can change all pixels of the picture, except forthe region of pixels comprising Conversation Participant's 50 face, intoa uniform color (i.e. white, blue, gray, etc.) so that the region ofpixels comprising Conversation Participant's 50 face becomes prominentand Insignificant Content 910 becomes suppressed or removed. ActivityDetector 160 can perform similar picture processing on other pictures inSub-stream of Digital Pictures 145. This way, the processed Sub-streamof Digital Pictures 145 would include only Conversation Participant's 50face without Insignificant Content 910. In another example, ActivityDetector 160 can extract the region of pixels comprising ConversationParticipant's 50 face from a picture of the Sub-stream of DigitalPictures 145, The extracted region of pixels can then be stored backinto the original picture replacing or overwriting all of its originalpixels. The extracted region of pixels can alternatively be stored intoa new picture a plurality of which may form a new Sub-stream of DigitalPictures 145 comprising Conversation Participant's 50 face. ActivityDetector 160 can perform similar picture processing on other pictures inSub-stream of Digital Pictures 145. In some aspects, Activity Detector160 can store the processed pictures into the Sub-stream of DigitalPictures 145 so that both original and processed pictures are availablein separate channels or repositories within Sub-stream of DigitalPictures 145. In addition to the previously described Picture Recognizer163 that itself includes segmentation functionalities, any picturesegmentation techniques can be utilized solely, in part, or incombination with other techniques in extracting or filtering personsand/or objects of interest from pictures of Sub-stream of DigitalPictures 145. Examples of picture segmentation techniques includethresholding, clustering, region-growing, edge detection, curvepropagation, level sets, graph partitioning, model-based segmentation,trainable segmentation (i.e. artificial neural networks, etc.), and/orothers. Extracting or filtering persons and/or objects of interest canoptionally be performed within another disclosed element (i.e.Picture-capturing Device 140, etc.) or by an additional element insteadof within Activity Detector 160 in alternate embodiments.

Referring to FIG. 3B, an embodiment of Activity Detector 160 extractingor filtering speech and/or sounds of interest is illustrated. This way,learning of conversations can focus on Conversation Participants' 50verbal expressions or communication regardless of and across differentacoustic backgrounds, ambient noises, and/or other insignificant sounds.As sound can be captured in various environments, before or as part ofthe extraction or filtering, Activity Detector 160 can perform denoisingof entire Sub-stream of Digital Sound Samples 155. Noise may include anysignal that degrades the quality of speech or sounds of interest such asequipment related noise, electrical or electromagnetic noise, orenvironmental noise. Examples of denoising techniques include AdaptiveWiener Filtering, Spectral Subtraction Methods (i.e. cepstral meannormalization, etc.), Spectral Restoration (i.e. speech enhancement,etc.), Harmonic Decomposition, Nonnegative Matrix Factorization (NMF),and/or others. In one example, Sub-stream of Digital Sound Samples 155may include Conversation Participant's 50 speech and ambient sounds suchas sound of water waves, laughter of nearby persons, music, and/or othersounds as shown, Activity Detector 160 can detect ConversationParticipant's 50 speech using Speech/Sound Recognizer 165 and/or othertechniques. Once detected, Activity Detector 160 can change all soundsamples of Sub-stream of Digital Sound Samples 155, except for the soundsamples comprising Conversation Participant's 50 speech, into silence sothat Conversation Participant's 50 speech becomes prominent andinsignificant Sounds 920 become suppressed or removed. This way, theprocessed Sub-stream of Digital Sound Samples 155 would include onlyspeech of Conversation Participant 50 without insignificant Sounds 920.In some aspects, Activity Detector 160 can change (i.e. reduce, etc.)the intensities of all sound samples of Sub-stream of Digital SoundSamples 155, except for the sound samples comprising ConversationParticipant's 50 speech, so that Conversation Participant's 50 speechbecomes prominent and insignificant Sounds 920 become suppressed orremoved. In further aspects, since Conversation Participant's 50 soundsmay be highest in amplitude, Activity Detector 160 can reduce (i.e.reduce to zero, etc.) the intensities of sound samples that are below acertain amplitude or intensity threshold, so that ConversationParticipant's 50 speech becomes prominent and Insignificant Sounds 920become suppressed or removed. In yet some aspects, since sounds canoverlap in time, Activity Detector 160 can change the intensities of allsound samples of Sub-stream of Digital Sound Samples 155, including thesound samples comprising Conversation Participant's 50 speech, so thatConversation Participant's 50 speech becomes prominent and InsignificantSounds 920 become suppressed or removed. In another example, ActivityDetector 160 can extract sound samples comprising ConversationParticipant's 50 speech from Sub-stream of Digital Sound Samples 155.The extracted sound samples can then be stored back into Sub-stream ofDigital Sound Samples 155 replacing all of its original sound samples.The extracted sound samples can alternatively be stored into a newSub-stream of Digital Sound Samples 155 comprising ConversationParticipant's 50 speech. In some aspects, Activity Detector 160 canstore the extracted sound samples into Sub-stream of Digital SoundSamples 155 so that both original and extracted sound samples areavailable in separate channels or repositories within Sub-stream ofDigital Sound Samples 155. In addition to the previously describedSpeech/Sound Recognizer 165 that itself includes segmentationfunctionalities, any speech or sound segmentation techniques can beutilized solely, in part, or in combination with other techniques inextracting or filtering speech and/or sounds of interest from Sub-streamof Digital Sound Samples 155. Examples of speech or sound segmentationtechniques include whole-word models, subword models, decompositionmodels, phonotactic models, any of the aforementioned denoisingtechniques, and/or others. Extracting or filtering speech and/or soundsof interest can optionally be performed within another disclosed element(i.e. Sound-capturing Device 150, etc.) or by an additional elementinstead of within Activity Detector 160 in alternate embodiments.

Referring to FIG. 4, an embodiment of Knowledge Structuring Unit 110 isillustrated. Knowledge Structuring Unit 110 comprises the functionalityfor structuring the knowledge of one or more conversations, and/or otherfunctionalities, Knowledge Structuring Unit 110 comprises thefunctionality for correlating Conversational Activities 210. KnowledgeStructuring Unit 110 comprises the functionality for creating orgenerating a Round of Conversational Exchange 200 and storing one ormore Conversational Activities 210 and/or other data (i.e. Extra Info250 [later described], etc.) into the Round of Conversational Exchange200. As such, Round of Conversational Exchange 200 (also referred to asRound of Cony, etc.) comprises the functionality for storing one or moreConversational Activities 210 and/or other data. Once created orgenerated, Rounds of Conversational Exchange 200 can be used in/asneurons, nodes, vertices, or other elements in any of the knowledge ordata structures/arrangements (i.e. neural networks, graphs, sequences,etc.) used for storing the knowledge of conversations and facilitatinglearning functionalities herein.

In some embodiments, Knowledge Structuring Unit 110 may receive one ormore Conversational Activities 210 of Conversation Participants 50 a and50 b from Activity Detectors 160 a and 160 b, respectively. KnowledgeStructuring Unit 110 may then correlate the one or more ConversationalActivities 210 of Conversation Participant 50 a with the one or moreConversational Activities 210 of Conversation Participant Kb and storethe correlated Conversational Activities 210 into a Round ofConversational Exchange 200 as shown. In effect, Round of ConversationalExchange 200 includes a unit of knowledge (i.e. correlatedConversational Activities 210, etc.) of how one Conversation Participant50 acted relative to another Conversation Participant 50, and viceversa, in a part of a conversation. When Conversational Activities 210with similar content, structure, and/or other properties are detectedinvolving a user in the future, the learned Conversational Activities210 of one or more Conversation Participants 50 stored in Rounds ofConversational Exchange 200 can be anticipated, thereby simulating aconversation with one or more AI Conversation Participants 55 as laterdescribed. In one example, Conversation Participant 50 a may speak whileConversation Participant 50 b observes (i.e. silent facial expressions,silent body movements, motionless silence, etc.) in a particular part ofa conversation, therefore, a Round of Conversational Exchange 200 mayinclude Conversation Participant's 50 a speaking Conversational Activity210 correlated with Conversation Participant's 50 b silent facialexpressions Conversational Activity 210. In another example, bothConversation Participants 50 a and 50 b may observe in a particular partof a conversation, therefore, a Round of Conversational Exchange 200 mayinclude Conversation Participant's 50 a silent body movementsConversational Activity 210 correlated with Conversation Participant's50 b silent facial expressions Conversational Activity 210. In a furtherexample, both Conversation Participants 50 a and 50 b may speak in aparticular part of a conversation, therefore, a Round of ConversationalExchange 200 may include Conversation Participant's 50 a speakingConversational Activity 210 correlated with Conversation Participant's50 b speaking Conversational Activity 210.

Referring to FIG. 5, another embodiment of Knowledge Structuring Unit110 is illustrated. In some aspects, the timing of ConversationalActivities 210 of different Conversation Participants 50 may coincide.In other aspects, the timing of Conversational Activities 210 ofdifferent Conversation Participants 50 can partially coincide oroverlap. In further aspects, the number of Conversational Activities 210of one Conversation Participant 50 may equal the number ofConversational Activities 210 of another Conversation Participant 50. Infurther aspects, the number of Conversational Activities 210 of oneConversation Participant 50 can differ from the number of ConversationalActivities 210 of another Conversation Participant 50. In general, aRound of Conversational Exchange 200 may include any number, types,timing, and/or other properties of Conversational Activities 210 of anynumber of Conversation Participants 50 arranged in any conceivablecombination.

Referring to FIG. 6A, an exemplary embodiment of Round of ConversationalExchange 200 comprising one Conversational Activity 210 of ConversationParticipant 50 a and one Conversational Activity 210 of ConversationParticipant 50 b that temporally coincide (i.e. ConversationalActivities 210 of both Conversation Participants 50 may start and end atthe same time, etc.) is illustrated. For example, speakingConversational Activity 210 of Conversation Participant 50 a correlatedwith silent body movements Conversational Activity 210 of ConversationParticipant 50 b can be stored in a Round of Conversational Exchange200.

Referring to FIG. 6B, an exemplary embodiment of Round of ConversationalExchange 200 comprising one Conversational Activity 210 of ConversationParticipant 50 a and one Conversational Activity 210 of ConversationParticipant 50 b that temporally partially coincide is illustrated.

Referring to FIG. 6C, an exemplary embodiment of Round of ConversationalExchange 200 comprising two Conversational Activities 210 ofConversation Participant 50 a and two Conversational Activities 210 ofConversation Participant 50 b that temporally coincide (i.e.Conversational Activities 210 of both Conversation Participants 50 maystart and end at same times, etc.) is illustrated. For example, speakingConversational Activity 210 of Conversation Participant 50 a correlatedwith silent facial expressions Conversational Activity 210 ofConversation Participant 50 b and subsequent motionless silenceConversational Activity 210 of Conversation Participant 50 a correlatedwith subsequent speaking Conversational Activity 210 of ConversationParticipant 50 b can be stored in a Round of Conversational Exchange200.

Referring to FIG. 6D, an exemplary embodiment of Round of ConversationalExchange 200 comprising two Conversational Activities 210 ofConversation Participant 50 a and two Conversational Activities 210 ofConversation Participant 50 b that temporally partially coincide isillustrated.

Referring to FIG. 6E, an exemplary embodiment of Round of ConversationalExchange 200 comprising two or more Conversational Activities 210 ofConversation Participant 50 a and two or more Conversational Activities210 of Conversation Participant 50 b is illustrated. Some of theConversational Activities 210 may temporally coincide as shown. In oneexample, two Conversational Activities 210 of Conversation Participant50 a correlated with three Conversational Activities 210 of ConversationParticipant 50 b can be stored in a Round of Conversational Exchange200. Specifically, in this example, speaking and silent body movementsConversational Activities 210 of Conversation Participant 50 acorrelated with silent facial expressions, silent body movements, andspeaking Conversational Activities 210 of Conversation Participant 50 bcan be stored in a Round of Conversational Exchange 200. In anotherexample, two Conversational Activities 210 of Conversation Participant50 a correlated with one Conversational Activity 210 of ConversationParticipant 50 b can be stored in a Round of Conversational Exchange200.

Referring to FIG. 6F, another exemplary embodiment of Round ofConversational Exchange 200 comprising two or more ConversationalActivities 210 of Conversation Participant 50 a and two or moreConversational Activities 210 of Conversation Participant 50 b isillustrated. Some of the Conversational Activities 210 may temporallypartially coincide as shown.

Referring to FIGS. 7A and 7B, exemplary embodiments of Round ofConversational Exchange 200 comprising one Conversational Activity 210of Conversation Participant 50 a or 50 b is illustrated. Such Rounds ofConversational Exchange 200 can be used to store some ConversationalActivities 210 of Conversation Participants 50 a and 50 b while omittingothers. For example, speaking Conversational Activities 210 ofConversation Participants 50 a and 50 b can be stored in Rounds ofConversational Exchange 200 while observing Conversational Activities210 (i.e. silent facial expressions, silent body movements, motionlesssilence, etc.) can be omitted. In some aspects, Rounds of ConversationalExchange 200 comprising a single Conversational Activity 210 maythemselves be correlated or interconnected indicating theirrelationships.

Referring to FIG. 7C, an exemplary embodiment of Round of ConversationalExchange 200 comprising one Conversational Activity 210 of ConversationParticipant 50 a and one Conversational Activity 210 of ConversationParticipant 50 b that temporally extend (i.e. Conversational Activity210 of one Conversation Participant 50 starts where ConversationalActivity 210 of another Conversation Participant 50 ends, etc.) oneanother is illustrated. Such Rounds of Conversational Exchange 200 canbe used to store some Conversational Activities 210 of ConversationParticipants 50 a and 50 b while omitting others. For example, speakingConversational Activities 210 of Conversation Participants 50 a and 50 bcan be stored in a Round of Conversational Exchange 200 while observingConversational Activities 210 (i.e. silent facial expressions, silentbody movements, motionless silence, etc.) can be omitted.

Referring to FIG. 7D, an exemplary embodiment of Round of ConversationalExchange 200 comprising one Conversational Activity 210 of ConversationParticipant 50 a and one Conversational Activity 210 of ConversationParticipant 50 b that temporally extend one another and overlap isillustrated.

Referring to FIG. 7E, an exemplary embodiment of Round of ConversationalExchange 200 comprising one Conversational Activity 210 of ConversationParticipant 50 a, one Conversational Activity 210 of ConversationParticipant 50 b, and one Conversational Activity 210 of ConversationParticipant 50 c (not shown) that temporally coincide (i.e.Conversational Activities 210 of all Conversation Participants 50 maystart and end at the same time, etc.) is illustrated, Round ofConversational Exchange 200 may include Conversational Activities 210 ofany number of Conversation Participants 50, For example, speakingConversational Activity 210 of Conversation Participant 50 a correlatedwith silent body movements Conversational Activity 210 of ConversationParticipant 50 b and silent facial expressions Conversational Activity210 of Conversation Participant 50 c can be stored in a Round ofConversational Exchange 200. Any of the previously described partiallycoinciding, overlapping, and/or extending Conversational Activities 210can similarly be used in Round of Conversational Exchange 200 comprisingConversational Activities 210 of more than two Conversation Participants50, Round of Conversational Exchange 200 comprising ConversationalActivities 210 of more than two Conversation Participants 50 can be usedto learn conversations among more than two persons. As such, Round ofConversational Exchange 200 comprising Conversational Activities 210 ofmore than two Conversation Participants 50 may include a unit ofknowledge (i.e. correlated Conversational Activities 210, etc.) of howmultiple Conversation Participants 50 acted relative to otherConversation Participants 50 in a part of a conversation.

One of ordinary skill in art will understand that Rounds ofConversational Exchange 200 in the preceding figures are describedmerely as examples of a variety of possible implementations and that anynumber or arrangement of Conversational Activities 210 can be used in aRound of Conversational Exchange 200 in alternate embodiments. Also, itshould be understood that the various data structures such asConversational Activity 210, Round of Conversational Exchange 200,and/or others are used to organize the disclosed elements in particularembodiments, and that other additional data structures can be includedas needed, or some of the disclosed ones can be excluded, or acombination thereof can be utilized in alternate embodiments. In oneexample, Rounds of Conversational Exchange 200 as containers forConversational Activities 210 can be omitted in which caseConversational Activities 210 can be stored directly into nodes ofneural network, graph, sequence, and/or other knowledge or datastructure. In another example, Conversational Activities 210 ascontainers for Sub-streams of Digital Pictures 145 and/or Sub-streams ofDigital Sound Samples 155 can be omitted in which case Sub-streams ofDigital Pictures 145 and/or Sub-streams of Digital Sound Samples 155 canbe stored directly into Rounds of Conversational Exchange 200 or intonodes of neural network, graph, sequence, and/or other knowledge or datastructure.

Referring to FIG. 8A-8C, embodiments of Extra Information 250 (alsoreferred to as Extra Info 250) are illustrated. Extra Info 250 comprisesthe functionality for storing any information useful in simulating AIConversation Participant 55 (later described), and/or otherfunctionalities. In one example, Extra Info 250 can be stored in orassociated with a Sub-stream of Digital Pictures 145 or Sub-stream ofDigital Sound Samples 155 as shown in FIG. 8A. In another example, ExtraInfo 250 can be stored in or associated with Conversational Activity 210as shown in FIG. 8B. In a further example, Extra Info 250 can be storedin or associated with a Round of Conversational Exchange 200 as shown inFIG. 8C. In general, Extra Info 250 related to any other element cansimilarly be stored in or associated with that element. In someembodiments, Knowledge Structuring Unit 110 can obtain and/or storeExtra Info 250 in its creation of Round of Conversational Exchange 200.In other embodiments, Activity Detector 160 can obtain and/or storeExtra Info 250 in its creation of Conversational Activity 210, Examplesof Extra info 250 include time information, location information,observed information, contextual information, and/or other information.Any information can be utilized that can provide additional informationfor enhanced simulation of AI Conversation Participant 55 (laterdescribed). Which information is stored in Extra Info 250 can be set bya user, by AIIM system administrator, or automatically by the system.Extra Info 250 may include or be referred to as contextual information,and vice versa. Therefore, these terms may be used interchangeablyherein. In some aspects, time information (i.e. time stamp, etc.) storedin Extra Info 250 can be useful in anticipating AI ConversationParticipant's 55 Conversational Activities 210 related to a specifictime period as people sometimes talk specific topics at certain parts ofday, month, year, and/or other time periods. Time information can beobtained from the system clock or other time source. In one example,people may speak about how nice or bad a morning is early in the day andhow sleepy or tired they are late in the day. In another example, peoplemay speak about how expensive the monthly mortgage payment is in thebeginning of the month. In a further example, people may speak about sunand sunny days in the summer, about falling leaves and rain in the fall,and about snow and ice in the winter. In a further example, people mayspeak about specific holiday topics on particular dates of the year. Inother aspects, location information (i.e. latitude/longitude/altitudecoordinates, address, etc.) stored in Extra Info 250 can be useful inanticipating AI Conversation Participant's 55 Conversational Activities210 related to a specific place as people sometimes talk about specifictopics at certain places (i.e. frequently visited or other places,etc.). Location information can be obtained from a positioning system(i.e. radio signal triangulation in smartphones or tablets, GPScapabilities in some high-end digital cameras, etc.) if one isavailable. For example, people may speak about school related topics atschool, work related topics at work, religious topics at a house ofworship, and/or other topics related to other places. In furtheraspects, observed information stored in Extra Info 250 can be useful inanticipating AI Conversation Participant's 55 Conversational Activities210 related to a specific object or environment as people sometimes talkabout proximal objects or environments. An object or environment can berecognized by processing Sub-stream of Digital Pictures 145 and/orSub-stream of Digital Sound Samples 155. For example, the system mayrecognize a specific object or environment such as library, park, beach,gym, and/or others in Sub-stream of Digital Pictures 145 and/or inSub-stream of Digital Sound Samples 155. Any features, functionalities,and embodiments of Picture Recognizer 163 and/or Speech/Sound Recognizer165 can be utilized for such recognizing. For example, book shelvesrecognized in the background of Sub-stream of Digital Pictures 145 mayindicate a library or book store, trees recognized in the background ofSub-stream of Digital Pictures 145 may indicate a park, sound of wavesrecognized in Sub-stream of Digital Sound Samples 155 may indicate abeach, and/or others.

Referring to FIG. 9A, an embodiment of utilizing System for LearningAIIMs 100 implemented as a network service is illustrated.

Referring to FIG. 9B, an embodiment of internal structure of System forLearning AIIMs 100 implemented as a network service is illustrated.System for Learning AIIMs 100 comprises interconnected KnowledgeStructuring Unit 110, Knowledgebase 130, and Activity Detectors 160 aand 160 b. Some embodiments of Activity Detector 160 a may includePicture Recognizer 163 a and Speech/Sound Recognizer 165 a whereas someembodiments of Activity Detector 160 b may include Picture Recognizer163 b and Speech/Sound Recognizer 165 b. System for Learning AIIMs 100or any element thereof may reside or operate on Server 90 (i.e. alsoreferred to as remote computing device, etc.), which is accessible byUser Devices 80 a and 80 b over Network 95. User Devices 80 a and 80 bcomprise Picture-capturing Devices 140 a and 140 b, respectively, andSound-capturing Devices 150 a and 150 b, respectively. Server 90, andUser Devices 80 a and 80 b may include any features, functionalities,and embodiments of the previously described Computing Device 70. Otheradditional elements can be included as needed, or some of the disclosedones can be excluded, or a combination thereof can be utilized inalternate embodiments.

In some embodiments, System for Learning AIIMs 100 can learnconversations among Conversation Participants 50 in a video call. Avideo call may be facilitated by services or applications such as AppleFaceTime, Google Hangouts, Skype, Viber, and/or other video callservices or applications. Such services commonly use one or more Servers90 accessible over Network 95 (i.e. Internet, intranet, etc.) tofacilitate video calls for their users. Server 90 may be or include anytype or form of a remote computing device such as an application server,a network service server, a cloud server, a cloud, and/or other remotecomputing device. In addition to traditional networks (i.e. Internet,intranet, etc.), Network 95 may include any type or form of directconnection among User Devices 80 such as wired or wireless (i.e.Bluetooth, etc.) direct connection. Two or more ConversationParticipants 50 may engage in a video call using their respective UserDevices 80. User Device 80 comprises any computing, mobile,telecommunication, electronic, and/or other device that can facilitateAIIM functionalities. Examples of User Device 80 include a smartphone, apersonal computer, a mobile computer (i.e. tablet, laptop, etc.), and/orothers. User Device 80 may include any features, functionalities, andembodiments of the previously described Computing Device 70, Most modernUser Devices 80 comprise Picture-capturing Device 140 (i.e. built-incamera, etc.) and Sound-capturing Device 150 (i.e. built-in microphone,etc.). It should be understood that Server 90 does not have to be aseparate or central computing device in between User Devices 80 a and 80b, and that Server 90 or portions thereof can be implemented on one ormore of User Devices 80 a and/or 80 b. In some designs, Server 90 mayindeed be omitted, in which case all of its elements and functionalitieswould be distributed or performed on User Devices 80 a and/or 80 b. Insuch implementations, Knowledge Structuring Unit 110, Knowledgebase 130,Activity Detectors 160 a and 160 b, and/or other elements of System forLearning AIIMs 100 can reside on User Devices 80 a and/or 80 b dependingon design.

In other embodiments, System for Learning AIIMs 100 can learnconversations among Conversation Participants 50 in a text messaging ortextual chat. System for Learning AIIMs 100 may include a text inputdevice (i.e. keyboard, keypad, touch screen, etc.) instead of or inaddition to Picture-capturing Device 140 and/or Sound-capturing Device150. The text input device can be used for exchanging textualexpressions or communication among Conversation Participants 50. In suchimplementations, System for Learning AIIMs 100 can learn textualexpressions or communication instead of visual and/or verbal expressionsor communication. For example, Conversation Participants 50 may engagein text messaging or textual chat using their respective User Devices80. Text messaging or textual chat may be facilitated by services orapplications such as Apple iMessage, Google Messenger, Skype InstantMessaging, Textra SMS, IRC, and/or others. Such services commonly useone or more Servers 90 accessible over Network 95 (i.e. Internet,intranet, etc.) to facilitate text messaging or textual chat for theirusers, although, text messaging or textual chat can be peer-to-peerwithout a server. As Conversation Participants 50 exchange textualexpressions or communication, System for Learning AIIMs 100 can learnthe textual conversations among the Conversation Participants 50 asdescribed herein with respect to learning visual and verbal expressionsor communication. Specifically, Conversational Activity 210 wouldinclude text instead of Stream of Digital Pictures 143 and/or Stream ofDigital Sound Samples 153. In some aspects, learning textual expressionsor communication may be easier to implement since it is easier or evenapparent to detect conversational activities in textual communication(i.e. a conversational activity may include a single or group of textmessages, etc.). Also, less processing is required for text relative tovisual and verbal data. Stream of Digital Pictures 143, Stream ofDigital Sound Samples 153, some parts of Activity Detector 160, and/orother elements can be optionally omitted in System for Learning AIIMs100 that learns textual conversations.

Referring to FIG. 10A, an embodiment of utilizing System for LearningAIIMs 100 embedded in Host Device 98 is illustrated.

Referring to FIG. 10B, an embodiment of internal structure of System forLearning AIIMs 100 embedded in Host Device 98 is illustrated. System forLearning AIIMs 100 comprises interconnected Knowledge Structuring Unit110, Knowledgebase 130, and Activity Detector 160. Some embodiments ofActivity Detector 160 may include Picture Recognizer 163 andSpeech/Sound Recognizer 165, System for Learning AIIMs 100 may reside oroperate on Host Device 98. System for Learning AIIMs 100 may utilizeHost Device's 98 Picture-capturing Device 140 and Sound-capturing Device150 to implement its functionalities. Host Device 98 may include anyfeatures, functionalities, and embodiments of the previously describedComputing Device 70. Other additional elements can be included asneeded, or some of the disclosed ones can be excluded, or a combinationthereof can be utilized in alternate embodiments.

In some aspects, System for Learning AIIMs 100 can learn conversationsamong Conversation Participants 50 engaged in a conversation near HostDevice 98. Host Device 98 comprises any computing, mobile,telecommunication, electronic, and/or other device that can facilitateAIIM functionalities. Examples of Host Devices 98 include a televisiondevice, a set-top box, a disc or other media player (i.e. DVD orBlue-ray player, etc.), a gaming device (i.e. Microsoft Xbox, SonyPlayStation, etc.), a smartphone (i.e. Apple iPhone, Samsung Galaxy,etc.), a mobile computer (i.e. tablet or laptop computer, etc.), a stillor motion picture camera, and/or others. Host Device 98 may include oneor more Picture-capturing Devices 140 (i.e. built-in cameras, etc.) andSound-capturing Devices 150 (i.e. built-in microphones, etc.). In thecase of more than one Picture-capturing Device 140 and Sound-capturingDevice 150, each Conversation Participant 50 may have a dedicatedPicture-capturing Device 140 and Sound-capturing Device 150, and Systemfor Learning AIIMs 100 may operate as previously described. In the caseof a single Picture-capturing Device 140 and a single Sound-capturingDevice 150, Activity Detector 160 can detect a plurality (i.e. one foreach Conversation Participant 50, etc.) of parallel Sub-streams ofDigital Pictures 145 from a single Stream of Digital Pictures 143captured by the single Picture-capturing Device 140. Similarly, ActivityDetector 160 can detect a plurality (i.e. one for each ConversationParticipant 50, etc.) of parallel Sub-streams of Digital Sound Samples155 from a single Stream of Digital Sound Samples 153 captured by thesingle Sound-capturing Device 150. A parallel Sub-stream of DigitalPictures 145 may include visual expressions or communication of one ofthe Conversation Participants 50 in the single Stream of DigitalPictures 143. A parallel Sub-stream of Digital Sound Samples 155 mayinclude verbal expressions or communication of one of the ConversationParticipants 50 in the single Stream of Digital Sound Samples 153.Activity Detector 160 can utilize the previously describedfunctionalities of Picture Recognizer 163 (i.e. facial recognition,etc.) to recognize and/or track multiple Conversation Participants 50 ina single Stream of Digital Pictures 143. Similarly, Activity Detector160 can utilize the previously described functionalities of Speech/SoundRecognizer 165 (i.e. speaker dedicated recognition, etc.) to recognizeand/or track multiple Conversation Participants' 50 speeches or soundsin a single Stream of Digital Sound Samples 153.

In some designs, instead of being captured by Picture-capturing Device140 in real time, the single Stream of Digital Pictures 143 may be orinclude any stored stream of digital pictures captured by anypicture-capturing device. Similarly, instead of being captured bySound-capturing Device 150 in real time, the single Stream of DigitalSound Samples 153 may be or include any stored stream of digital soundsamples captured by any sound-capturing device. As such, ActivityDetector 160 can detect a plurality (i.e. one for each ConversationParticipant 50, etc.) of parallel Sub-streams of Digital Pictures 145from a single stored Stream of Digital Pictures 143 and a plurality(i.e. one for each Conversation Participant 50, etc.) of parallelSub-streams of Digital Sound Samples 155 from a single stored Stream ofDigital Sound Samples 153. The single stored Stream of Digital Pictures143 and/or the single stored Stream of Digital Sound Samples 153 maycommonly be included in a file (i.e. video file, etc.) comprising theStream of Digital Pictures 143 and/or the Stream of Digital SoundSamples 153. Examples of such files include family videos, recordedvideo or phone conversations, news interviews, video databases (i.e.Youtube, Yahoo Video Search, Google Videos, etc.), and/or any othermaterial comprising a recording of a conversation among two or moreconversation participants. In one example, System for Learning AIIMs 100can learn conversations from one or more family videos selected by auser. In another example, System for Learning AIIMs 100 can traverseYoutube videos to learn conversations involving a particular person. Thevideos involving the person may be found by searching for the person'sname or other information. In a further example, System for LearningAIIMs 100 can traverse Youtube videos to learn conversations involvingsome or all persons depicted in some or all Youtube videos.

Referring to FIG. 11, the teaching presented by the disclosure can beimplemented to include various artificial intelligence models and/ortechniques. The disclosed devices, systems, and methods for learning andusing AIIMs are independent of the artificial intelligence model and/ortechnique used and any model and/or technique can be utilized tofacilitate the functionalities described herein. Examples of thesemodels and/or techniques include deep learning, supervised learning,unsupervised learning, neural networks (i.e. convolutional neuralnetwork, recurrent neural network, deep neural network, etc.),search-based, logic and/or fuzzy logic-based, optimization-based,tree/graph/other data structure-based, hierarchical, symbolic and/orsub-symbolic, evolutionary, genetic, multi-agent, deterministic,probabilistic, statistical, and/or other models and/or techniques.

In one example shown in Model A, the disclosed devices, systems, andmethods for learning and using AIIMs may include a neural network (alsoreferred to as artificial neural network, etc.). As such, machinelearning, knowledge representation or structure, pattern recognition,decision making, and/or other artificial intelligence functionalitiesmay include a network of Nodes 852 (also referred to as neurons in thecontext of neural networks, etc.) and Connections 853 similar to that ofa brain. Node 852 can store any data, object, data structure, and/orother item, or reference thereto. Node 852 may also include a functionfor transforming or manipulating any data, object, data structure,and/or other item. Examples of such transformation functions includemathematical functions (i.e. addition, subtraction, multiplication,division, sin, cos, log, derivative, integral, etc.), objectmanipulation functions (i.e. creating an object, modifying an object,deleting an object, appending objects, etc.), data structuremanipulation functions (i.e. creating a data structure, modifying a datastructure, deleting a data structure, creating a data field, modifying adata field, deleting a data field, etc.), and/or other transformationfunctions. Connection 853 can store or be associated with a value suchas a symbolic label or numeric attribute (i.e. weight, cost, capacity,length, etc.). A neural network can be utilized as a predictive modelingapproach in machine learning. A computational model can be utilized tocompute values from inputs based on a pre-programmed or learned functionor method. For example, a neural network may include one or more inputneurons that can be activated by inputs. Activations of these neuronscan then be passed on, weighted, and transformed by a function to otherneurons. Neural networks may range from those with only one layer ofsingle direction logic to multi-layer of multi-directional feedbackloops. A neural network can use weights to change the parameters of thenetwork's throughput. A neural network can learn by input from itsenvironment or from self-teaching using written-in rules. An exemplaryembodiment of a neural network (i.e. Neural Network 130 a, etc.) isdescribed later.

In another example shown in Model B, the disclosed devices, systems, andmethods for learning and using AIIMs may include a graph or graph-likedata structure. As such, machine learning, knowledge representation orstructure, pattern recognition, decision making, and/or other artificialintelligence functionalities may include Nodes 852 (i.e. vertices,points, etc.) and Connections 853 (i.e. edges, arrows, lines, arcs,etc.) organized as a graph. A graph can be utilized as a predictivemodeling approach in machine learning. In general, any Node 852 in agraph can be connected to any other Node 852. A Connection 853 mayinclude unordered pair of Nodes 852 in an undirected graph or orderedpair of Nodes 852 in a directed graph. Nodes 852 can be part of thegraph structure or external entities represented by indices orreferences. Nodes 852, Connections 853, and/or operations of a graph mayinclude any features, functionalities, and embodiments of theaforementioned Nodes 852, Connections 853, and/or operations of a neuralnetwork, and vice versa. An exemplary embodiment of a graph (i.e. Graph130 b, etc.) is described later.

In a further example shown in Model C, the disclosed devices, systems,and methods for learning and using AIIMs may include a tree or tree-likestructure. As such, machine learning, knowledge representation orstructure, pattern recognition, decision making, and/or other artificialintelligence functionalities may include Nodes 852 and Connections 853(i.e. references, edges, etc.) organized as a tree. A tree can beutilized as a predictive modeling approach in machine learning. Ingeneral, a Node 852 in a tree can be connected to any number (i.e.including zero, etc.) of children Nodes 852 (i.e. similar to a tree,etc.). In some aspects, a collection of trees can be utilized where eachtree may represent a set of related conversational paths such as, forexample, paths concerning a topic or concept, Nodes 852, Connections853, and/or operations of a tree may include any features,functionalities, and embodiments of the aforementioned Nodes 852,Connections 853, and/or operations of a neural network and/or graph, andvice versa.

In a further example shown in Model D, the disclosed devices, systems,and methods for learning and using AIIMs may include a sequence orsequence-like structure. As such, machine learning, knowledgerepresentation or structure, pattern recognition, decision making,and/or other artificial intelligence functionalities may include astructure of Nodes 852 and Connections 853 organized as a sequence. Insome aspects, Connections 853 may be optionally omitted from a sequence.A sequence can be utilized as a predictive modeling approach in machinelearning. In some aspects, a sequence can be used to store a singleconversation. In other aspects, a sequence can be used to store multipleconcatenated conversations. Nodes 852, Connections 853, and/oroperations of a sequence may include any features, functionalities, andembodiments of the aforementioned Nodes 852, Connections 853, and/oroperations of a neural network, graph, and/or tree, and vice versa. Anexemplary embodiment of a sequence (i.e. Collection of Sequences 130 c,Sequence 133, etc.) is described later.

In yet another example the disclosed devices, systems, and methods forlearning and using AIIMs may include a search-based model and/ortechnique. As such, machine learning, knowledge representation orstructure, pattern recognition, decision making, and/or other artificialintelligence functionalities may include searching through a collectionof possible solutions. For example, a search method can search through aneural network, graph, tree, list, or other data structure that includesdata elements of interest. A search may use heuristics to limit thesearch for solutions by eliminating choices that are unlikely to lead tothe goal. Heuristic techniques may provide a best guess solution. Asearch can also include optimization. For example, a search may beginwith a guess and then refine the guess incrementally until no morerefinements can be made. In a further example, the disclosed devices,systems, and methods for learning and using AIIMs may includelogic-based model and/or technique. As such, machine learning, knowledgerepresentation or structure, pattern recognition, decision making,and/or other artificial intelligence functionalities can use formal orother type of logic. Logic based models may involve making inferences orderiving conclusions from a set of premises. As such, a logic basedsystem can extend existing knowledge or create new knowledgeautomatically using inferences, Examples of the types of logic that canbe utilized include propositional or sentential logic that compriseslogic of statements which can be true or false; first-order logic thatallows the use of quantifiers and predicates and that can express factsabout objects, their properties, and their relations with each other;fuzzy logic that allows degrees of truth to be represented as a valuebetween 0 and 1 rather than simply 0 (false) or 1 (true), which can beused for uncertain reasoning; subjective logic that comprises a type ofprobabilistic logic that may take uncertainty and belief into account,which can be suitable for modeling and analyzing situations involvinguncertainty, incomplete knowledge and different world views; and/orother types of logic. In a further example the disclosed devices,systems, and methods for learning and using AIIMs may include aprobabilistic model and/or technique. As such, machine learning,knowledge representation or structure, pattern recognition, decisionmaking, and/or other artificial intelligence functionalities can beimplemented to operate with incomplete or uncertain information whereprobabilities may affect outcomes. Bayesian network, among other models,is an example of a probabilistic tool used for purposes such asreasoning, learning, planning, perception, and/or others. One ofordinary skill in art will understand that the aforementioned artificialintelligence models and/or techniques are described merely as examplesof a variety of possible implementations, and that while all possibleartificial intelligence models and/or techniques are too voluminous todescribe, other artificial intelligence models and/or techniques knownin art are within the scope of this disclosure. One of ordinary skill inart will also recognize that an intelligent system may solve a specificproblem by using any model and/or technique that works such as, forexample, some systems can be symbolic and logical, some can besub-symbolic neural networks, some can be deterministic orprobabilistic, some can be hierarchical, some may include searchingtechniques, some may include optimization techniques, while others mayuse other or a combination of models and/or techniques. In general, anyartificial intelligence model and/or technique can be utilized that cansupport AIIM functionalities.

Referring to FIG. 12A-12C, exemplary embodiments of interconnectedRounds of Conversational Exchange 200 and updating weights ofConnections 853 are illustrated. As shown for example in FIG. 12A, Roundof Conversational Exchange 200 ca is connected to Round ofConversational Exchange 200 cb and Round of Conversational Exchange 200cc by Connection 853 e and Connection 853 f, respectively, Each ofConnection 853 e and Connection 853 f may include or be associated withoccurrence count, weight, any parameter, and/or other data. The numberof occurrences may track or store the number of observations that aRound of Conversational Exchange 200 was followed by another Round ofConversational Exchange 200 indicating a connection or relationshipbetween them. For example, Round of Conversational Exchange 200 ca wasfollowed by Round of Conversational Exchange 200 cb 10 times asindicated by the number of occurrences of Connection 853 e. Also, Roundof Conversational Exchange 200 ca was followed by Round ofConversational Exchange 200 cc 15 times as indicated by the number ofoccurrences of Connection 853 f, The weight of Connection 853 e can becalculated or determined as the number of occurrences of Connection 853e divided by the sum of occurrences of all connections (i.e. Connection853 e and Connection 853 f, etc.) originating from Round ofConversational Exchange 200 ca. Therefore, the weight of Connection 853e can be calculated or determined as 10/(10+15)=0.4, for example. Also,the weight of Connection 853 f can be calculated or determined as15/(10+15)=0.6, for example, Therefore, the sum of weights of Connection853 e, Connection 853 f, and/or any other Connections 853 originatingfrom Round of Conversational Exchange 200 ca may equal to 1 or 100%. Asshown for example in FIG. 12B, in the case that Round of ConversationalExchange 200 cd is inserted and an observation is made that Round ofConversational Exchange 200 cd follows Round of Conversational Exchange200 ca, Connection 853 g can be created between Round of ConversationalExchange 200 ca and Round of Conversational Exchange 200 cd. Theoccurrence count of Connection 853 g can be set to 1 and weightdetermined as 1/(10+15+1)=0.038. The weights of all other connections(i.e. Connection 853 e, Connection 853 f, etc.) originating from Roundof Conversational Exchange 200 ca may be updated to account for thecreation of Connection 853 g. Therefore, the weight of Connection 853 ecan be updated as 10/(10+15+1)=0.385. The weight of Connection 853 f canalso be updated as 15/(10+15+1)=0.577. As shown for example in FIG. 12C,in the case that an additional occurrence of Connection 853 e isobserved (i.e. Round of Conversational Exchange 200 cb followed Round ofConversational Exchange 200 ca, etc.), occurrence count of Connection853 e and weights of all connections (i.e. Connection 853 e, Connection853 f, and Connection 853 g, etc.) originating from Round ofConversational Exchange 200 ca may be updated to account for thisobservation. The occurrence count of Connection 853 e can be increasedby 1 and its weight updated as 11/(11+15+1)=0.407. The weight ofConnection 853 f can also be updated as 15/(11+15+1)=0.556. The weightof Connection 853 g can also be updated as 1/(11+15+1)=0.037.

In some embodiments, Connection 853 may connect not only Rounds ofConversational Exchange 200, but also Conversational Activities 210and/or other elements. For example, a Conversational Activity 210 in oneRound of Conversational Exchange 200 may be connected by Connection 853to a Conversational Activity 210 in another Round of ConversationalExchange 200. In general, Connections 853 can connect any Rounds ofConversational Exchange 200, any Conversational Activities 210, and/orother elements.

Referring to FIG. 13, an exemplary embodiment of learning Rounds ofConversational Exchange 200 using Neural Network 130 a is illustrated.Neural Network 130 a includes a number of neurons or Nodes 852interconnected by Connections 853 as previously described. Rounds ofConversational Exchange 200 are shown instead of Nodes 852 to simplifythe illustration as Node 852 includes a Round of Conversational Exchange200, for example. Therefore, Rounds of Conversational Exchange 200 andNodes 852 can be used interchangeably herein depending on context. Itshould be noted that Node 852 may include other elements and/orfunctionalities instead of or in addition to Round of ConversationalExchange 200. Rounds of Conversational Exchange 200 may be applied ontoNeural Network 130 a individually or collectively in a learning ortraining process. In some designs, Neural Network 130 a comprises anumber of Layers 854 each of which may include one or more Rounds ofConversational Exchange 200. Rounds of Conversational Exchange 200 insuccessive Layers 854 can be connected by Connections 853. Connection853 may include or be associated with occurrence count, weight, anyparameter, and/or other data as previously described. Neural Network 130a may include any number of Layers 854 to accommodate conversationscomprising any number of Rounds of Conversational Exchange 200. Ineffect, Neural Network 130 a may store Rounds of Conversational Exchange200 interconnected by Connections 853 where following a path through theNeural Network 130 a can later be used to simulate a conversation. Itshould be understood that, in some embodiments, Rounds of ConversationalExchange 200 in one Layer 854 of Neural Network 130 a need not beconnected only with Rounds of Conversational Exchange 200 in asuccessive Layer 854, but also in any other Layer 854, thereby creatingshortcuts (i.e. shortcut Connections 853, etc.) through Neural Network130 a. A Round of Conversational Exchange 200 can also be connected toitself such as, for example, in recurrent neural networks. In general,any Round of Conversational Exchange 200 can be connected with any otherRound of Conversational Exchange 200 anywhere else in Neural Network 130a. In further embodiments, back-propagation of any data or informationcan be implemented. In one example, back-propagation of similarity (i.e.similarity index, etc.) of compared Rounds of Conversational Exchange200 in a path through Neural Network 130 a can be implemented. Inanother example, back-propagation of errors can be implemented. Suchback-propagations can then be used to adjust occurrence counts and/orweights of Connections 853 for better future predictions, for example.Any other back-propagation can be implemented for other purposes. Anycombination of Nodes 852 (i.e. Nodes 852 comprising Round ofConversational Exchange 200, etc.), Connections 853, Layers 854, and/orother elements or techniques can be implemented in alternateembodiments. Neural Network 130 a may include any type or form of aneural network known in art such as a feed-forward neural network, aback-propagating neural network, a recurrent neural network, aconvolutional neural network, deep neural network, and/or othersincluding a custom neural network.

In some embodiments, Knowledge Structuring Unit 110 creates or generatesRounds of Conversational Exchange 200 and the system applies them ontoNeural Network 130 a, thereby implementing learning of Rounds ofConversational Exchange 200. The term apply or applying may refer tostoring, copying, inserting, updating, or other similar action,therefore, these terms may be used interchangeably herein depending oncontext. The system can perform Substantial Similarity Comparisons 125(later described) of a Round of Conversational Exchange 200 fromKnowledge Structuring Unit 110 with Rounds of Conversational Exchange200 in a corresponding Layer 854 of Neural Network 130 a, If asubstantially similar Round of Conversational Exchange 200 is not foundin the corresponding Layer 854 of Neural Network 130 a, the system mayinsert (i.e. copy, store; etc.) the Round of Conversational Exchange 200from Knowledge Structuring Unit 110 into the corresponding Layer 854 ofNeural Network 130 a, and create a Connection 853 to the inserted Roundof Conversational Exchange 200 from a Round of Conversational Exchange200 in a prior Layer 854 including assigning an occurrence count to thenew Connection 853, calculating a weight of the new Connection 853, andupdating any other Connections 853 originating from the Round ofConversational Exchange 200 in the prior Layer 854, On the other hand,if a substantially similar Round of Conversational Exchange 200 is foundin the corresponding Layer 854 of Neural Network 130 a, the system mayupdate occurrence count and weight of Connection 853 to that Round ofConversational Exchange 200 from a Round of Conversational Exchange 200in a prior Layer 854, and update any other Connections 853 originatingfrom the Round of Conversational Exchange 200 in the prior Layer 854.

For example, the system can perform Substantial Similarity Comparisons125 of Round of Conversational Exchange 200 aa from KnowledgeStructuring Unit 110 with Rounds of Conversational Exchange 200 in acorresponding Layer 854 a of Neural Network 130 a. In the case that asubstantially similar match is found between Round of ConversationalExchange 200 aa and Round of Conversational Exchange 200 ba, the systemmay perform no action since Round of Conversational Exchange 200 ba isthe initial Round of Conversational Exchange 200. The system can thenperform Substantial Similarity Comparisons 125 of Round ofConversational Exchange 200 ab from Knowledge Structuring Unit 110 withRounds of Conversational Exchange 200 in a corresponding Layer 854 b ofNeural Network 130 a. In the case that a substantially similar match isfound between Round of Conversational Exchange 200 ab and Round ofConversational Exchange 200 bb, the system may update occurrence countand weight of Connection 853 a between Round of Conversational Exchange200 ba and Round of Conversational Exchange 200 bb, and update weightsof other Connections 853 originating from Round of ConversationalExchange 200 ba as previously described. The system can then performSubstantial Similarity Comparisons 125 of Round of ConversationalExchange 200 ac from Knowledge Structuring Unit 110 with Rounds ofConversational Exchange 200 in a corresponding Layer 854 c of NeuralNetwork 130 a. In the case that a substantially similar match is notfound, the system may insert Round of Conversational Exchange 200 bcinto Layer 854 c and copy Round of Conversational Exchange 200 ac intothe inserted Round of Conversational Exchange 200 bc. The system mayalso create Connection 853 b between Round of Conversational Exchange200 bb and Round of Conversational Exchange 200 bc with occurrence countof 1 and weight calculated based on the occurrence count as previouslydescribed. The system may also update weights of other Connections 853(one in this example) originating from Round of Conversational Exchange200 bb as previously described. The system can then perform SubstantialSimilarity Comparisons 125 of Round of Conversational Exchange 200 adfrom Knowledge Structuring Unit 110 with Rounds of ConversationalExchange 200 in a corresponding Layer 854 d of Neural Network 130 a. Inthe case that a substantially similar match is not found, the system mayinsert Round of Conversational Exchange 200 bd into Layer 854 d and copyRound of Conversational Exchange 200 ad into the inserted Round ofConversational Exchange 200 bd. The system may also create Connection853 c between Round of Conversational Exchange 200 bc and Round ofConversational Exchange 200 bd with occurrence count of 1 and weightof 1. The system can then perform Substantial Similarity Comparisons 125of Round of Conversational Exchange 200 ae from Knowledge StructuringUnit 110 with Rounds of Conversational Exchange 200 in a correspondingLayer 854 e of Neural Network 130 a. In the case that a substantiallysimilar match is not found, the system may insert Round ofConversational Exchange 200 be into Layer 854 e and copy Round ofConversational Exchange 200 ae into the inserted Round of ConversationalExchange 200 be. The system may also create Connection 853 d betweenRound of Conversational Exchange 200 bd and Round of ConversationalExchange 200 be with occurrence count of 1 and weight of 1. Applying anyadditional Rounds of Conversational Exchange 200 from KnowledgeStructuring Unit 110 onto Neural Network 130 a follows similar logic orprocess as the above-described.

Substantial Similarity Comparison 125 (also referred to simply assimilarity comparison or other similar reference) comprises thefunctionality for comparing or matching Rounds of ConversationalExchange 200 or portions thereof, and/or other functionalities.Substantial Similarity Comparison 125 comprises the functionality forcomparing or matching Conversational Activities 210 or portions thereof.Substantial Similarity Comparison 125 comprises the functionality forcomparing or matching Sub-streams of Digital Pictures 145 or portionsthereof. Substantial Similarity Comparison 125 comprises thefunctionality for comparing or matching Sub-streams of Digital SoundSamples 155 or portions thereof. Substantial Similarity Comparison 125comprises the functionality for comparing or matching text (i.e.characters, words, phrases, etc.), pictures, sounds, data, and/or otherelements or portions thereof. Substantial Similarity Comparison 125 mayinclude functions, rules, and/or logic for performing matching orcomparisons and for determining that while a perfect match is not found,a similar or substantially similar match has been found. Whilesubstantial similarity may imply a substantial level of similarity,substantial similarity may also, depending on context, include anysimilarity, however high or low, as defined by the rules for substantialsimilarity. The rules for substantial similarity or substantiallysimilar match can be defined by a user, by AIIM system administrator, orautomatically by the system based on experience, testing, inquiry,analysis, synthesis, or other techniques, knowledge, or input. In somedesigns, Substantial Similarity Comparison 125 comprises thefunctionality to automatically define appropriately strict rules fordetermining substantial similarity of the compared elements. SubstantialSimilarity Comparison 125 can therefore set, reset, and/or adjust thestrictness of the rules for finding or determining substantialsimilarity between the compared elements, thereby fine tuningSubstantial Similarity Comparison 125 so that the rules for determiningsubstantial similarity are appropriately strict. In some aspects,Substantial Similarity Comparison 125 can determine substantialsimilarity of compared elements if their similarity exceeds a threshold(i.e. similarity threshold, etc.). In other aspects, SubstantialSimilarity Comparison 125 can determine substantial similarity ofcompared elements if their difference is lower than a threshold (i.e.difference threshold, etc.).

In determining substantial similarity of Rounds of ConversationalExchange 200, Substantial Similarity Comparison 125 may compare one ormore Conversational Activities 210 or portions thereof of one Round ofConversational Exchange 200 with one or more Conversational Activities210 or portions thereof of another Round of Conversational Exchange 200.In some embodiments, total equivalence is achieved when allConversational Activities 210 or portions thereof of the compared Roundsof Conversational Exchange 200 match. If total equivalence is not found,Substantial Similarity Comparison 125 may attempt to determinesubstantial similarity. In some aspects, substantial similarity can beachieved when most of the Conversational Activities 210 or portions(i.e. Sub-streams of Digital Pictures 145, Sub-streams of Digital SoundSamples 155, etc.) thereof of the compared Rounds of ConversationalExchange 200 match or substantially match, in other aspects, substantialsimilarity can be achieved when at least a threshold number orpercentage of Conversational Activities 210 or portions thereof of thecompared Rounds of Conversational Exchange 200 match or substantiallymatch. Similarly, substantial similarity can be achieved when a numberor percentage of matching or substantially matching ConversationalActivities 210 or portions thereof of the compared Rounds ofConversational Exchange 200 exceeds a threshold. In further aspects,substantial similarity can be achieved when all but a threshold numberor percentage of Conversational Activities 210 or portions thereof ofthe compared Rounds of Conversational Exchange 200 match orsubstantially match. Such thresholds can be defined by a user, by AIIMsystem administrator, or automatically by the system based onexperience, testing, inquiry, analysis, synthesis, or other techniques,knowledge, or input. In one example, substantial similarity can beachieved when at least 1, 2, 3, 4, or any other threshold number ofConversational Activities 210 or portions thereof of the compared Roundsof Conversational Exchange 200 match or substantially match. Similarly,substantial similarity can be achieved when the number of matching orsubstantially matching Conversational Activities 210 or portions thereofof the compared Rounds of Conversational Exchange 200 exceeds 1, 2, 3,4, or any other threshold number. In another example, substantialsimilarity can be achieved when at least 10%, 21%, 30%, 49%, 66%, 89%,93%, or any other percentage of Conversational Activities 210 orportions thereof of the compared Rounds of Conversational Exchange 200match or substantially match. Similarly, substantial similarity can beachieved when the percentage of matching or substantially matchingConversational Activities 210 or portions thereof of the compared Roundsof Conversational Exchange 200 exceeds 10%, 21%, 30%, 49%, 66%, 89%,93%, or any other threshold percentage. In other embodiments,substantial similarity of the compared Rounds of Conversational Exchange200 can be achieved in terms of matches or substantial matches in moreimportant (i.e. as indicated by importance index [later described],etc.) Conversational Activities 210 or portions thereof, therebytolerating mismatches in less important Conversational Activities 210 orportions thereof. In one example, substantial similarity can be achievedwhen matches or substantial matches are found in speaking ConversationalActivities 210 or portions thereof of the compared Rounds ofConversational Exchange 200, thereby tolerating mismatches in observingConversational Activities 210 (i.e. silent facial expressions, silentbody movements, motionless silence, etc.) or portions thereof. In somedesigns, Substantial Similarity Comparison 125 can be configured to omitany Conversational Activity 210 or portions thereof from the comparison.In one example, some or all motionless silence Conversational Activities210 or portions thereof can be omitted. In another example, some or allabsence from the conversation Conversational Activities 210 or portionsthereof, or other Conversational Activities 210 or portions thereof canbe omitted. In further embodiments, substantial similarity can beachieved taking into account duration, type, and/or other features ofConversational Activities 210 of the compared Rounds of ConversationalExchange 200. In one example, substantial similarity can be achieved ifthe durations of one or more Conversational Activities 210 of thecompared Rounds of Conversational Exchange 200 match or substantiallymatch. In another example, substantial similarity can be achieved if thetypes (i.e. speaking, silent facial expressions, silent body movements,motionless silence, absence from the conversation, etc.) of one or moreConversational Activities 210 of the compared Rounds of ConversationalExchange 200 match or substantially match. In some aspects, SubstantialSimilarity Comparison 125 can compare durations, types, and/or otherfeatures of Conversational Activities 210 as an initial check beforeproceeding to further detailed comparisons.

Substantial Similarity Comparison 125 can automatically adjust (i.e.increase or decrease) the strictness of the rules for determiningsubstantial similarity of Rounds of Conversational Exchange 200. In someaspects, such adjustment in strictness can be done by SubstantialSimilarity Comparison 125 in response to determining that totalequivalence match had not been found. In other aspects, an adjustment instrictness can be done by Substantial Similarity Comparison 125 inresponse to determining that substantially similar match had not beenfound. Substantial Similarity Comparison 125 can keep adjusting thestrictness of the rules until a substantially similar match is found.All the rules or settings of substantial similarity can be set, reset,or adjusted by Substantial Similarity Comparison 125 in response toanother strictness level determination. For example, SubstantialSimilarity Comparison 125 may attempt to find a match in a certainpercentage (i.e. 95%, etc.) of Conversational Activities 210 or portionsthereof from the compared Rounds of Conversational Exchange 200. If thecomparison does not provide a substantially similar match, SubstantialSimilarity Comparison 125 may decide to decrease the strictness of therules to find a substantially similar match. In response, SubstantialSimilarity Comparison 125 may attempt to find fewer matchingConversational Activities 210 or portions thereof than in the previousattempt using stricter rules. If the comparison still does not provide asubstantially similar match, Substantial Similarity Comparison 125 maydetermine to further decrease (i.e. down to a certain minimum strictnessor threshold, etc.) the strictness by requiring fewer ConversationalActivities 210 or portions thereof to match, thereby further increasinga chance of finding a substantially similar match. In further aspects,an adjustment in strictness can be done by Substantial SimilarityComparison 125 in response to determining that multiple substantiallysimilar matches had been found. Substantial Similarity Comparison 125can keep adjusting the strictness of the rules until a best of thesubstantially similar matches is found. For example, SubstantialSimilarity Comparison 125 may attempt to find a match in a certainpercentage (i.e. 70%, etc.) of Conversational Activities 210 or portionsthereof from the compared Rounds of Conversational Exchange 200. If thecomparison provides a number of substantially similar matches,Substantial Similarity Comparison 125 may decide to increase thestrictness of the rules to decrease the number of substantially similarmatches. In response, Substantial Similarity Comparison 125 may attemptto find more matching Conversational Activities 210 or portions thereofin addition to the earlier found Conversational Activities 210 orportions thereof to limit the number of substantially similar matches.If the comparison still provides more than one substantially similarmatch, Substantial Similarity Comparison 125 may determine to furtherincrease the strictness by requiring additional ConversationalActivities 210 or portions thereof to match, thereby further narrowingthe number of substantially similar matches until a best substantiallysimilar match is found.

In determining substantial similarity of Conversational Activities 210or portions thereof, Substantial Similarity Comparison 125 may compareSub-stream of Digital Pictures 145 or portions thereof of oneConversational Activity 210 with Sub-stream of Digital Pictures 145 orportions thereof of another Conversational Activity 210. Also, indetermining substantial similarity of Conversational Activities 210 orportions thereof, Substantial Similarity Comparison 125 may compareSub-stream of Digital Sound Samples 155 or portions thereof of oneConversational Activity 210 with Sub-stream of Digital Sound Samples 155or portions thereof of another Conversational Activity 210. In someembodiments, total equivalence is achieved when Sub-streams of DigitalPictures 145 or portions thereof and Sub-streams of Digital SoundSamples 155 or portions thereof of the compared ConversationalActivities 210 match. If total equivalence is not found, SubstantialSimilarity Comparison 125 may attempt to determine substantialsimilarity. In some aspects, substantial similarity can be achieved whenmost of the portions (i.e. pictures, features, regions of pixels,pixels, etc.) of Sub-streams of Digital Pictures 145 and/or portions(i.e. words, features, sound samples, etc.) of Sub-streams of DigitalSound Samples 155 of the compared Conversational Activities 210 match orsubstantially match. In other aspects, substantial similarity can beachieved when at least a threshold number or percentage of portions ofSub-streams of Digital Pictures 145 and/or portions of Sub-streams ofDigital Sound Samples 155 of the compared Conversational Activities 210match or substantially match. Similarly, substantial similarity can beachieved when the number or percentage of matching or substantiallymatching portions of Sub-streams of Digital Pictures 145 and/or portionsof Sub-streams of Digital Sound Samples 155 of the comparedConversational Activities 210 exceeds a threshold. In further aspects,substantial similarity can be achieved when all but a threshold numberor percentage of portions of Sub-streams of Digital Pictures 145 and/orportions of Sub-streams of Digital Sound Samples 155 of the comparedConversational Activities 210 match or substantially match. Suchthresholds can be defined by a user, by AIIM system administrator, orautomatically by the system based on experience, testing, inquiry,analysis, synthesis, or other techniques, knowledge, or input. In oneexample, substantial similarity can be achieved when at least 1, 2, 17,38, 124, 4299, or any other threshold number of portions of Sub-streamsof Digital Pictures 145 and/or portions of Sub-streams of Digital SoundSamples 155 of the compared Conversational Activities 210 match orsubstantially match. Similarly, substantial similarity can be achievedwhen the number of matching or substantially matching portions ofSub-streams of Digital Pictures 145 and/or portions of Sub-streams ofDigital Sound Samples 155 of the compared Conversational Activities 210exceeds 1, 2, 17, 38, 124, 4299, or any other threshold number. Inanother example, substantial similarity can be achieved when at least9%, 23%, 29%, 41%, 63%, 79%, 92%, or any other percentage of portions ofSub-streams of Digital Pictures 145 and/or portions of Sub-streams ofDigital Sound Samples 155 of the compared Conversational Activities 210match or substantially match. Similarly, substantial similarity can beachieved when the percentage of matching or substantially matchingportions of Sub-streams of Digital Pictures 145 and/or portions ofSub-streams of Digital Sound Samples 155 of the compared ConversationalActivities 210 exceeds 9%, 23%, 29%, 41%, 63%, 79%, 92%, or any otherthreshold percentage. In other embodiments, weights can be assigned toSub-streams of Digital Pictures 145 or portions thereof and/orSub-streams of Digital Sound Samples 155 or portions thereof indicatingtheir importance in the comparison. In one example, 1 (i.e. 100%, etc.)can be assigned to Sub-streams of Digital Sound Samples 155 or portionsthereof and 0 (i.e. 0%, etc.) can be assigned to Sub-streams of DigitalPictures 145 or portions thereof indicating that Substantial SimilarityComparison 125 relies solely on comparison of Sub-streams of DigitalSound Samples 155 or portions thereof in which case comparison ofSub-streams of Digital Pictures 145 or portions thereof can be omitted.In another example, 0.8 (i.e. 80%, etc.) can be assigned to Sub-streamsof Digital Sound Samples 155 or portions thereof and 0.2 (i.e. 20%,etc.) can be assigned to Sub-streams of Digital Pictures 145 or portionsthereof indicating that Substantial Similarity Comparison 125 reliesmostly on comparison of Sub-streams of Digital Sound Samples 155 orportions thereof. In a further example, 0 (i.e. 0%, etc.) can beassigned to Sub-streams of Digital Sound Samples 155 or portions thereofand 1 (i.e. 100%, etc.) can be assigned to Sub-streams of DigitalPictures 145 or portions thereof indicating that Substantial SimilarityComparison 125 relies solely on comparison of Sub-streams of DigitalPictures 145 or portions thereof in which case comparison of Sub-streamsof Digital Sound Samples 155 or portions thereof can be omitted. Anyweight or importance can be assigned to any of the Sub-streams ofDigital Sound Samples 155 or portions thereof, Sub-streams of DigitalPictures 145 or portions thereof, and/or other elements herein. Similarweights can be utilized for any of the elements described herein. Infurther embodiments, substantial similarity can be achieved taking intoaccount the type and/or other features of Conversational Activities 210.For example, any observing Conversational Activity 210 (i.e. silentfacial expressions, silent body movements, motionless silence, etc.) maybe found to match another observing Conversational Activity 210.

Substantial Similarity Comparison 125 can automatically adjust (i.e.increase or decrease) the strictness of the rules for determiningsubstantial similarity of Conversational Activities 210. In someaspects, such adjustment in strictness can be done by SubstantialSimilarity Comparison 125 in response to determining that totalequivalence match had not been found. In other aspects, an adjustment instrictness can be done by Substantial Similarity Comparison 125 inresponse to determining that substantially similar match had not beenfound. Substantial Similarity Comparison 125 can keep adjusting thestrictness of the rules until a substantially similar match is found.All the rules or settings of substantial similarity can be set, reset,or adjusted by Substantial Similarity Comparison 125 in response toanother strictness level determination. For example, SubstantialSimilarity Comparison 125 may attempt to find a match in a certainpercentage (i.e. 88%, etc.) of the portions of Sub-streams of DigitalPictures 145 and/or portions of Sub-streams of Digital Sound Samples 155of the compared Conversational Activities 210, If the comparison doesnot provide a substantially similar match, Substantial SimilarityComparison 125 may decide to decrease the strictness of the rules tofind a substantially similar match. In response, Substantial SimilarityComparison 125 may attempt to find fewer matching portions ofSub-streams of Digital Pictures 145 and/or portions of Sub-streams ofDigital Sound Samples 155 than in the previous attempt using stricterrules. If the comparison still does not provide a substantially similarmatch, Substantial Similarity Comparison 125 may determine to furtherdecrease the strictness (i.e. down to a certain minimum strictness orthreshold, etc.) by requiring fewer portions of Sub-streams of DigitalPictures 145 and/or portions of Sub-streams of Digital Sound Samples 155to match, thereby further increasing a chance of finding a substantiallysimilar match. In further aspects, an adjustment in strictness can bedone by Substantial Similarity Comparison 125 in response to determiningthat multiple substantially similar matches had been found. SubstantialSimilarity Comparison 125 can keep adjusting the strictness of the rulesuntil a best of the substantially similar matches is found. For example,Substantial Similarity Comparison 125 may attempt to find a match in acertain percentage (i.e. 74%, etc.) of portions of Sub-streams ofDigital Pictures 145 and/or portions of Sub-streams of Digital SoundSamples 155 of the compared Conversational Activities 210. If thecomparison provides a number of substantially similar matches,Substantial Similarity Comparison 125 may decide to increase thestrictness of the rules to decrease the number of substantially similarmatches. In response, Substantial Similarity Comparison 125 may attemptto find more matching portions of Sub-streams of Digital Pictures 145and/or portions of Sub-streams of Digital Sound Samples 155 in additionto the earlier found portions to limit the number of substantiallysimilar matches. If the comparison still provides more than onesubstantially sirnilar match, Substantial Similarity Comparison 125 maydetermine to further increase the strictness by requiring additionalportions of Sub-streams of Digital Pictures 145 and/or portions ofSub-streams of Digital Sound Samples 155 to match, thereby furthernarrowing the number of substantially similar matches until a bestsubstantially similar match is found.

In determining substantial similarity of Sub-streams of Digital Pictures145 or portions thereof and/or Sub-streams of Digital Sound Samples 155or portions thereof, Substantial Similarity Comparison 125 can usevarious techniques examples of which are described below.

In some embodiments, in determining substantial similarity ofSub-streams of Digital Sound Samples 155 or portions thereof,Substantial Similarity Comparison 125 may compare one or more wordsrecognized from one Sub-stream of Digital Sound Samples 155 with one ormore words recognized from another Sub-stream of Digital Sound Samples155. Any features, functionalities, and embodiments of the previouslydescribed Speech/Sound Recognizer 165 can be used in such word or speechrecognition. In some aspects, total equivalence is found when all wordsrecognized from one Sub-stream of Digital Sound Samples 155 match allwords recognized from another Sub-stream of Digital Sound Samples 155.In other aspects, if total equivalence is not found, SubstantialSimilarity Comparison 125 may attempt to determine substantialsimilarity. In one example, substantial similarity can be achieved whenmost of the words recognized from the compared Sub-streams of DigitalSound Samples 155 match. In another example, substantial similarity canbe achieved when at least a threshold number (i.e. 1, 2, 4, 7, 34, etc.)or percentage (i.e. 33%, 58%, 72%, 99%, etc.) of words of the comparedSub-streams of Digital Sound Samples 155 match. Similarly, substantialsimilarity can be achieved when the number or percentage of matchingwords of the compared Sub-streams of Digital Sound Samples 155 exceeds athreshold number (i.e. 1, 2, 4, 7, 34, etc.) or a threshold percentage(i.e. 33%, 58%, 72%, 99%, etc.). In a further example, substantialsimilarity can be achieved when all but a threshold number or percentageof words of the compared Sub-streams of Digital Sound Samples 155 match.Such thresholds can be defined by a user, by AIIM system administrator,or automatically by the system based on experience, testing, inquiry,analysis, synthesis, and/or other techniques, knowledge or input. Infurther aspects, Substantial Similarity Comparison 125 can utilize theorder of words for determining substantial similarity of Sub-streams ofDigital Sound Samples 155. For example, substantial similarity can beachieved when matches are found with respect to front-most words,thereby tolerating mismatches in later words. Any order of words can befavored in alternate implementations. In further aspects, SubstantialSimilarity Comparison 125 can utilize the type of words for determiningsubstantial similarity of Sub-streams of Digital Sound Samples 155. Forexample, substantial similarity can be achieved when matches are foundwith respect to substantive or longer words such as nouns, verbs,adjectives, and/or others, thereby tolerating mismatches inless-substantive or shorter words such as definite and indefinitearticles (i.e. the, a, an, etc.), demonstratives (i.e. this, that,etc.), possessive determiners (i.e. my, your, their, etc.), quantifiers(i.e. many, few, several, etc.), distributive determiners (i.e. each,any, etc.), and/or others. In further aspects, Substantial SimilarityComparison 125 can utilize the importance (i.e. as indicated byimportance index [later described], etc.) of words for determiningsubstantial similarity of Sub-streams of Digital Sound Samples 155. Forexample, substantial similarity can be achieved when matches are foundwith respect to more important words such as the above-describedsubstantive, longer, and/or other words, thereby tolerating mismatchesin less important words such as less-substantive, shorter, and/or otherwords. In further aspects, Substantial Similarity Comparison 125 canomit some of the words from the comparison in determining substantialsimilarity of Sub-streams of Digital Sound Samples 155. In one example,less-substantive or shorter words can be omitted from comparison. Inanother example, later or rear-most words can be omitted fromcomparison. In general, any word can be omitted from comparison. Infurther aspects, Substantial Similarity Comparison 125 can utilizesemantic conversion to account for variations of words in determiningsubstantial similarity of Sub-streams of Digital Sound Samples 155, Inone example, Sub-stream of Digital Sound Samples 155 may include a word“home”, In addition to searching for the exact word in a comparedSub-stream of Digital Sound Samples 155, Substantial SimilarityComparison 125 can employ semantic conversion and attempt to match“house”, “residence”, “dwelling”, “place”, or other semantically similarvariations of the word with a meaning “home”. In another example,Sub-stream of Digital Sound Samples 155 may include a word “buy”. Inaddition to searching for the exact word in a compared Sub-stream ofDigital Sound Samples 155, Substantial Similarity Comparison 125 canemploy semantic conversion and attempt to match “buying”, “bought”, orother semantically similar variations of the word with a meaning “buy”in different tenses. Any other grammatical analysis or transformationcan be utilized to cover the full scope of word variations. In somedesigns, semantic conversion can be implemented using a thesaurus ordictionary. In another example, semantic conversion can be implementedusing a table where each row comprises semantically similar variationsof a word. In further aspects, Substantial Similarity Comparison 125 canutilize a language model for understanding or interpreting the conceptscontained in the words and compare the concepts instead of or inaddition to the words in determining substantial similarity ofSub-streams of Digital Sound Samples 155, A language model may alsoprovide context to distinguish among words and phrases that soundsimilar. Examples of language models include unigram model, n-grammodel, neural network language model, bag of words model, and/or others,Any of the techniques for matching of words can similarly be used formatching of concepts.

Substantial Similarity Comparison 125 can automatically adjust (i.e.increase or decrease) the strictness of the rules for determiningsubstantial similarity of Sub-streams of Digital Sound Samples 155 usingwords. In some aspects, such adjustment in strictness can be done bySubstantial Similarity Comparison 125 in response to determining thattotal equivalence match had not been found. In other aspects, anadjustment in strictness can be done by Substantial SimilarityComparison 125 in response to determining that substantially similarmatch had not been found. Substantial Similarity Comparison 125 can keepadjusting the strictness of the rules until a substantially similarmatch is found. All the rules or settings of substantial similarity canbe set, reset, or adjusted by Substantial Similarity Comparison 125 inresponse to another strictness level determination. For example,Substantial Similarity Comparison 125 may attempt to find a match in acertain percentage (i.e. 95%, etc.) of the recognized words from thecompared Sub-streams of Digital Sound Samples 155. If the comparisondoes not provide a substantially similar match using such strictness,Substantial Similarity Comparison 125 may decide to decrease thestrictness of the rules to find a substantially similar match. Inresponse, Substantial Similarity Comparison 125 may attempt to findfewer matching words than in the previous attempt using stricter rules.If the comparison still does not provide a substantially similar match,Substantial Similarity Comparison 125 may determine to further decreasethe strictness (Le, down to a certain minimum strictness or threshold,etc.) by requiring fewer words to match, thereby further increasing achance of finding a substantially similar match.

Where a reference to a word is used herein it should be understood thata portion of a word or a collection of words can be used instead of orin addition to the word. In one example, instead of or in addition towords, individual characters (i.e. letters, digits, symbols, eta) and/orother portions of a word can be compared. In another example, instead ofor in addition to words, phrases, sentences, and/or other collections ofwords can be compared. As such, any operations, rules, logic, and/orfunctions operating on words similarly apply to any portion of a wordand/or any collection of words. In a further example, where one or morefront-most words are used in the comparison as previously described, oneor more front-most characters and/or other portions of a word can beused in an alternate implementation of the comparison involving portionsof a word. In a further example, where comparison strictness isincreased by using one or more additional words in the comparison aspreviously described, additional one or more characters and/or otherportions of a word can be used in an alternate implementation of thecomparison involving portions of a word. In general, whole words,portions of a word, and/or collections of words, including anyoperations thereon, can be combined to arrive at desired results. Someor all of the above-described rules, logic, and/or techniques can beutilized alone or in combination with each other or with other rules,logic, and/or techniques, One of ordinary skill in art will recognizethat other techniques known in art for determining similarity of streamsof digital sound samples and/or other data sequences that would be toovoluminous to describe are within the scope of this disclosure.

In other embodiments, in determining substantial similarity ofSub-streams of Digital Sound Samples 155 or portions thereof,Substantial Similarity Comparison 125 can compare one or more featuresfrom one Sub-stream of Digital Sound Samples 155 with one or morefeatures from another Sub-stream of Digital Sound Samples 155. A feature(also referred to as sound feature or other similar reference, etc.) mayinclude a collection of sound samples of a stream of digital soundsamples. For example, a feature may include sound samples in time framesof 5, 10, 15, 20, 25, 30, etc. milliseconds. In general, any collectionof sound samples covering any time frame can be utilized. Some of thesteps or elements in a typical feature oriented system includepre-processing, feature extraction, acoustic modeling, languagemodeling, and/or others, or a combination thereof, each of which mayinclude its own sub-steps or sub-elements depending on the application.Acoustic features can be used for classification of non-verbal vocaloutbursts such as laughter or sighs whereas linguistic features can beused to transcribe the linguistic message such as words, phrases, orsentences, Examples of feature extraction techniques includeMel-Frequency Cepstral Coefficients, Wavelet Based Features,Non-Negative Matrix Factorization, and/or others. Once features of thecompared Sub-streams of Digital Sound Samples 155 are known, SubstantialSimilarity Comparison 125 can compare the features to determinesubstantial similarity. Some of the previously described comparisontechniques for determining substantial similarity of Sub-streams ofDigital Sound Samples 155 using words can similarly be used forfeatures. In some aspects, total equivalence is found when all featuresfrom one Sub-stream of Digital Sound Samples 155 match all features fromanother Sub-stream of Digital Sound Samples 155. In other aspects, iftotal equivalence is not found, Substantial Similarity Comparison 125may attempt to determine substantial similarity. In one example,substantial similarity can be achieved when most of the features fromthe compared Sub-streams of Digital Sound Samples 155 match. In anotherexample, substantial similarity can be achieved when at least athreshold number (i.e. 1, 5, 17, 33, 68, 114, etc.) or percentage (i.e.31%, 59%, 82%, 98%, etc.) of features from the compared Sub-streams ofDigital Sound Samples 155 match. Similarly, substantial similarity canbe achieved when the number or percentage of matching features from thecompared Sub-streams of Digital Sound Samples 155 exceeds a thresholdnumber (i.e. 1, 5, 17, 33, 68, 114, etc.) or a threshold percentage(i.e. 31%, 59%, 82%, 98%, etc.). In a further example, substantialsimilarity can be achieved when all but a threshold number or percentageof features from the compared Sub-streams of Digital Sound Samples 155match. Such thresholds can be defined by a user, by AIIM systemadministrator, or automatically by the system based on experience,testing, inquiry, analysis, synthesis, and/or other techniques,knowledge or input. In further aspects, Substantial SimilarityComparison 125 can utilize the order of features for determiningsubstantial similarity of Sub-streams of Digital Sound Samples 155. Forexample, substantial similarity can be achieved when matches are foundwith respect to front-most features, thereby tolerating mismatches inlater features. Any order of features can be favored in alternateimplementations. In further aspects, Substantial Similarity Comparison125 can utilize the type of features for determining substantialsimilarity of Sub-streams of Digital Sound Samples 155. For example,substantial similarity can be achieved when matches are found withrespect to substantive or longer features, thereby tolerating mismatchesin less-substantive or shorter features. In further aspects, SubstantialSimilarity Comparison 125 can utilize the importance (i.e. as indicatedby importance index [later described], etc.) of features for determiningsubstantial similarity of Sub-streams of Digital Sound Samples 155. Forexample, substantial similarity can be achieved when matches are foundwith respect to more important features such the above-describedsubstantive, longer, and/or other features, thereby toleratingmismatches in less important features such as less-substantive, shorter,and/or other features. In further aspects, Substantial SimilarityComparison 125 can omit some of the features from the comparison indetermining substantial similarity of Sub-streams of Digital SoundSamples 155. In one example, less-substantive or shorter features can beomitted from comparison. In another example, later or rear-most featurescan be omitted from comparison. In general, any feature can be omittedfrom comparison.

The previously described functionality of Substantial SimilarityComparison 125 for automatically adjusting (i.e. increasing ordecreasing) the strictness of the rules for determining substantialsimilarity of Sub-streams of Digital Sound Samples 155 using words cansimilarly be used with features. In some aspects, such adjustment instrictness can be done by Substantial Similarity Comparison 125 inresponse to determining that total equivalence match had not been found.In other aspects, an adjustment in strictness can be done by SubstantialSimilarity Comparison 125 in response to determining that substantiallysimilar match had not been found. For example, if the comparison doesnot provide a substantially similar match using certain strictness,Substantial Similarity Comparison 125 may decide to decrease thestrictness (i.e. down to a certain minimum strictness or threshold,etc.) and attempt to find fewer matching features than in the previousattempt using stricter rules.

Where a reference to a feature is used herein it should be understoodthat a portion of a feature or a collection of features can be usedinstead of or in addition to the feature. In one example, instead of orin addition to features, individual sound samples that constitute thefeature can be compared. In another example, instead of or in additionto features, collections of features can be compared. In a furtherexample, levels of features where a feature on one level includes one ormore features from another level (i.e. prior level, etc.) can becompared. As such, any operations, rules, logic, and/or functionsoperating on features similarly apply to any portion of a feature and/orany collection of features. In general, whole features, portions of afeature, and/or collections of features, including any operationsthereon, can be combined to arrive at desired results. Some or all ofthe above-described rules, logic, and/or techniques can be utilizedalone or in combination with each other or with other rules, logic,and/or techniques. Any of the previously described features,functionalities, and embodiments of Substantial Similarity Comparison125 for determining substantial similarity of Sub-streams of DigitalSound Samples 155 using words can similarly be used for features. One ofordinary skill in art will recognize that other techniques known in artfor determining similarity of streams of digital sound samples and/orother data sequences that would too voluminous to describe are withinthe scope of this disclosure.

In further embodiments, in determining substantial similarity ofSub-streams of Digital Sound Samples 155 or portions thereof,Substantial Similarity Comparison 125 can compare sound samples from oneSub-stream of Digital Sound Samples 155 with sound samples from anotherSub-stream of Digital Sound Samples 155. In some aspects, totalequivalence is found when all sound samples from one Sub-stream ofDigital Sound Samples 155 match all sound samples from anotherSub-stream of Digital Sound Samples 155. In other aspects, if totalequivalence is not found, Substantial Similarity Comparison 125 mayattempt to determine substantial similarity. In one example, substantialsimilarity can be achieved when most of the sound samples from thecompared Sub-streams of Digital Sound Samples 155 match. In anotherexample, substantial similarity can be achieved when at least athreshold number (i.e. 21, 85, 154, 297, 422, 699, etc.) or percentage(i.e. 29%, 48%, 69%, 96%, etc.) of sound samples from the comparedSub-streams of Digital Sound Samples 155 match. Similarly, substantialsimilarity can be achieved when the number or percentage of matchingsound samples from the compared Sub-streams of Digital Sound Samples 155exceeds a threshold number (i.e. 21, 85, 154, 297, 422, 699, etc.) or athreshold percentage (i.e. 29%, 48%, 69%, 96%, etc.). In a furtherexample, substantial similarity can be achieved when all but a thresholdnumber or percentage of sound samples from the compared Sub-streams ofDigital Sound Samples 155 match. Such thresholds can be defined by auser, by AIIM system administrator, or automatically by the system basedon experience, testing, inquiry, analysis, synthesis, and/or othertechniques, knowledge, or input. In further aspects, SubstantialSimilarity Comparison 125 can utilize the order of sound samples fordetermining substantial similarity of Sub-streams of Digital SoundSamples 155. For example, substantial similarity can be achieved whenmatches are found with respect to front-most sound samples, therebytolerating mismatches in later sound samples. Any order of sound samplescan be favored in alternate implementations. In further aspects,Substantial Similarity Comparison 125 can utilize the importance i.e. asindicated by importance index [later described], etc.) of sound samplesfor determining substantial similarity of Sub-streams of Digital SoundSamples 155. For example, substantial similarity can be achieved whenmatches are found with respect to more important sound samples such theabove-described front-most and/or other sound samples, therebytolerating mismatches in less important sound samples such as laterand/or other sound samples. In further aspects, Substantial SimilarityComparison 125 can omit some of the sound samples from the comparison indetermining substantial similarity of Sub-streams of Digital SoundSamples 155. In one example, later or rear-most sound samples can beomitted from comparison. In another example, every other sound samplecan be omitted from comparison (i.e. to reduce processing time, etc.).In general, any sound sample can be omitted from comparison. In furtheraspects, Substantial Similarity Comparison 125 can generally compare oneor more sound samples from one Sub-stream of Digital Sound Samples 155with one or more corresponding (i.e. similarly positioned, temporallyrelated, etc.) sound samples from another Sub-stream of Digital SoundSamples 155. In one example, a 78^(th) sound sample from one Sub-streamof Digital Sound Samples 155 can be compared with a 78^(th) sound samplefrom another Sub-stream of Digital Sound Samples 155. In anotherexample, the 78^(th) sound sample from one Sub-stream of Digital SoundSamples 155 can be compared with a number of samples around (i.e.preceding and/or following) the 78^(th) sound sample from anotherSub-stream of Digital Sound Samples 155. This way, flexibility can beimplemented in finding a matching sound sample if the samples in thecompared Sub-streams of Digital Sound Samples 155 are not perfectlyaligned. In some designs, adjustments can be made in selecting one ormore corresponding sound samples. In one example, Substantial SimilarityComparison 125 can make adjustments to account for variable lengths ofsilence periods in the compared Sub-streams of Digital Sound Samples155. In another example, Substantial Similarity Comparison 125 canutilize Dynamic Time Warping (DTW) and/or other techniques for comparingand/or aligning temporal sequences (i.e. Sub-stream of Digital SoundSamples 155, etc.) that may vary in time or speed in order to accountfor different speaking speeds. In further aspects, SubstantialSimilarity Comparison 125 can utilize collections or frames of soundsamples in determining substantial similarity of Sub-streams of DigitalSound Samples 155. For instance, a frame may include sound samples intime periods of 5, 10, 15, 20, 25, 30, etc. milliseconds. In general, aframe may include any number of sound samples covering any time period.In one example, substantial similarity can be achieved when most of theframes of the compared Sub-streams of Digital Sound Samples 155 match.In another example, substantial similarity can be achieved when at leasta threshold number (i.e. 37, 111, 228, 433, etc.) or percentage (i.e.39%, 48%, 68%, 75%, 99%, etc.) of frames of the compared Sub-streams ofDigital Sound Samples 155 match.

The previously described functionality of Substantial SimilarityComparison 125 for automatically adjusting (i.e. increasing ordecreasing) the strictness of the rules for determining substantialsimilarity of Sub-streams of Digital Sound Samples 155 using wordsand/or features can similarly be used with sound samples. In someaspects, such adjustment in strictness can be done by SubstantialSimilarity Comparison 125 in response to determining that totalequivalence match had not been found. In other aspects, an adjustment instrictness can be done by Substantial Similarity Comparison 125 inresponse to determining that a substantially similar match had not beenfound. For example, if the comparison does not provide a substantiallysimilar match using certain strictness, Substantial SimilarityComparison 125 may decide to decrease the strictness (i.e. down to acertain minimum strictness or threshold, etc.) and attempt to find fewermatching sound samples than in the previous attempt using stricterrules.

Where a reference to a sound sample is used herein it should beunderstood that a collection (i.e. frame, etc.) of sound samples can beused instead of or in addition to the sound sample. For example, insteadof or in addition to sound samples, collections of sound samples can becompared. As such, any operations, rules, logic, and/or functionsoperating on sound samples similarly apply to any collection of soundsamples. In general, sound samples and/or collections of sound samples,including any operations thereon, can be combined to arrive at desiredresults. Some or all of the above-described rules, logic, and/ortechniques can be utilized alone or in combination with each other orwith other rules, logic, and/or techniques. Any of the previouslydescribed features, functionalities, and embodiments of SubstantialSimilarity Comparison 125 for determining substantial similarity ofSub-streams of Digital Sound Samples 155 using words and/or features cansimilarly be used for sound samples. One of ordinary skill in art willrecognize that other techniques known in art for determining similarityof streams of digital sound samples and/or other data sequences thatwould too voluminous to describe are within the scope of thisdisclosure.

Other aspects or properties of digital sound or sound samples can betaken into account by Substantial Similarity Comparison 125 in soundcomparisons. Examples of such aspects or properties include amplitudeadjustment, sample rate or frequency adjustment, noise reduction, and/orothers. In some implementations, as digital sound can be captured byvarious sound sensing equipment, from various distances, and undervarious conditions, Substantial Similarity Comparison 125 can adjustvalues or levels of sound samples or otherwise manipulate the samplesbefore or during comparison. Such adjustment may include amplifying orreducing the values of one or more sound samples. For example,Substantial Similarity Comparison 125 can amplify all samples of oneSub-stream of Digital Sound Samples 155 to make it more comparable toanother Sub-stream of Digital Sound Samples 155, Substantial SimilarityComparison 125 can also incrementally adjust the sound samples such asamplifying or reducing the samples by a certain amount in each cycle ofcomparisons in order to find a substantially similar match at one of theincremental adjustment levels. In other implementations, SubstantialSimilarity Comparison 125 can re-sample (i.e. up-sample or down-sample)or otherwise transform a digital sound before or during comparison. Suchre-sampling or transformation may include increasing or decreasing thenumber of sound samples. For example, Substantial Similarity Comparison125 can increase or decrease the number of samples of a Sub-stream ofDigital Sound Samples 155 to equate its sample rate or frequency withsample rate or frequency of another Sub-stream of Digital Sound Samples155. Any publically available, custom, or other re-sampling technique orprogram can be utilized such as converting a stream of sound samples toan analog signal and re-sampling at a new rate, calculating the valuesof the new samples directly from the old samples using interpolation(i.e. constant, linear, polynomial, spline, etc.) or decimation (i.e. byan integer or rational factor, etc.), and/or others. In furtherimplementations, as digital sound can be captured in variousenvironments, Substantial Similarity Comparison 125 can performdenoising of the compared Sub-streams of Digital Sound Samples 155.Noise may include any signal that degrades the quality of sound such asequipment related noise, electrical or electromagnetic noise,environmental noise, and/or other noise. Any of the publicallyavailable, custom, or other denoising techniques or programs can beutilized such as Adaptive Wiener Filtering, Spectral Subtraction Methods(i.e. cepstral mean normalization), Spectral Restoration (i.e. speechenhancement), Harmonic Decomposition, Nonnegative Matrix Factorization(NMF), and/or others. Substantial Similarity Comparison 125 can performany other pre-processing or manipulation of digital sound or soundsamples before or during comparison.

In any of the comparisons involving digital sound or sound samples,Substantial Similarity Comparison 125 can utilize a threshold foracceptable number or percentage difference in determining a match foreach compared sound sample. A sound sample can be encoded using varioustechniques such as binary value (i.e. 8 bit, 16 bit, 24 bit, etc.),hexadecimal value, numerical value, and/or others. For instance, in a 16bit encoding scheme, each sound sample is encoded with a value or levelof 0-65536 or its binary equivalent. In one example, a threshold foracceptable difference (i.e. absolute difference, etc.) can be set at1000 for each of the sound samples. Therefore, a sample encoded with avalue of 30100 matches or is sufficiently similar to a compared sampleencoded with a value of 30883 because the difference in the samplesfalls within the acceptable difference threshold (i.e. 1000 in thisexample, etc.). Furthermore, a sample encoded with a value of 30100 doesnot match or is not sufficiently similar to a compared pixel encodedwith a value of 31155 because the difference in the samples fallsoutside the acceptable difference threshold, Any other number thresholdcan be used such as 1, 8, 82, 492, 1195, 5004, 13883, 33841, 57112,and/or others, A threshold for acceptable percentage difference cansimilarly be utilized such as 0.38%, 4%, 11%, 24%, 31%, 46%, 78%, and/orothers. A similar difference determination can be utilized in samplesencoded in any other encoding scheme. The aforementioned thresholds canbe defined by a user, by AIIM system administrator, or automatically bythe system based on experience, testing, inquiry, analysis, synthesis,or other techniques, knowledge, or input.

In some designs, in determining substantial similarity of Sub-streams ofDigital Pictures 145 or portions thereof, Substantial SimilarityComparison 125 can compare pictures from one Sub-stream of DigitalPictures 145 with pictures from another Sub-stream of Digital Pictures145. Any features, functionalities, and embodiments of the previouslydescribed Picture Recognizer 163 can be used in determining substantialsimilarity of Sub-streams of Digital Pictures 145. In some aspects,Substantial Similarity Comparison 125 can compare pictures from oneSub-stream of Digital Pictures 145 with corresponding (i.e. similarlypositioned, temporally related, etc.) pictures from another Sub-streamof Digital Pictures 145. In one example, a 67^(th) picture from oneSub-stream of Digital Pictures 145 can be compared with a 67^(th)picture from another Sub-stream of Digital Pictures 145. In anotherexample, the 67^(th) picture from one Sub-stream of Digital Pictures 145can be compared with a number of pictures around (i.e. preceding and/orfollowing) the 67^(th) picture from another Sub-stream of DigitalPictures 145. This way, flexibility can be implemented in finding asubstantially similar picture if the pictures in the comparedSub-streams of Digital Pictures 145 are not perfectly aligned. In otheraspects, Substantial Similarity Comparison 125 can utilize Dynamic TimeWarping (DTW) and/or other techniques know in art for comparing and/oraligning temporal sequences (i.e. Sub-streams of Digital Pictures 145,etc.) that may vary in time or speed. Once the corresponding (i.e.similarly positioned, temporally related, time warped/aligned, etc.)pictures of Sub-streams of Digital Pictures 145 are compared and theirsubstantial similarity determined using comparison techniques forindividual pictures described below, Substantial Similarity Comparison125 can utilize a threshold for the number or percentage of matchingpictures for determining substantial similarity of the comparedSub-streams of Digital Pictures 145. In one example, substantialsimilarity can be achieved when most of the pictures of the comparedSub-streams of Digital Pictures 145 match or substantially match. Inanother example, substantial similarity of Sub-streams of DigitalPictures 145 can be achieved when at least a threshold number (i.e. 28,74, 283, 322, 995, 874, 8028, etc.) or percentage (i.e. 29%, 33%, 58%,72%, 99%, etc.) of pictures of the compared Sub-streams of DigitalPictures 145 match or substantially match. Similarly, substantialsimilarity can be achieved when the number or percentage of matching orsubstantially matching pictures of the compared Sub-streams of DigitalPictures 145 exceeds a threshold number (i.e. 28, 74, 283, 322, 995,874, 8028, etc.) or a threshold percentage (i.e. 29%, 33%, 58%, 72%,99%, etc.), in a further example, substantial similarity of Sub-streamsof Digital Pictures 145 can be achieved when all but a threshold numberor percentage of pictures of the compared Sub-streams of DigitalPictures 145 match or substantially match. Such thresholds can bedefined by a user, by AIIM system administrator, or automatically by thesystem based on experience, testing, inquiry, analysis, synthesis, orother techniques, knowledge, or input. In some aspects, SubstantialSimilarity Comparison 125 can omit some of the pictures from thecomparison in determining substantial similarity of Sub-streams ofDigital Pictures 145.

In some embodiments, in determining substantial similarity of individualpictures (i.e. pictures from the compared Sub-streams of DigitalPictures 145, etc.), Substantial Similarity Comparison 125 can compareone or more features of one picture with one or more features of anotherpicture. A feature (also referred to as picture feature or other similarreference, etc.) may include a collection or region of pixels of apicture. Some of the steps or elements in a typical feature orientedsystem include pre-processing, feature extraction,detection/segmentation, decision-making, and/or others, or a combinationthereof, each of which may include its own sub-steps or sub-elementsdepending on the application. Examples of features that can be usedinclude lines, edges, ridges, corners, blobs, regions, and/or others.Examples of feature extraction techniques include Canny, Sobe, Kayyali,Harris & Stephens et al, SUSAN, Level Curve Curvature, FAST, Laplacianof Gaussian, Difference of Gaussians, Determinant of Hessian, MSER,PCBR, Grey-level Blobs, and/or others. Once features of the comparedpictures are known, Substantial Similarity Comparison 125 can comparethe features to determine substantial similarity. In some aspects, totalequivalence is found when all features of one picture match all featuresof another picture. In other aspects, if total equivalence is not found,Substantial Similarity Comparison 125 may attempt to determinesubstantial similarity. In one example, substantial similarity can beachieved when most of the features of the compared pictures match. Inanother example, substantial similarity can be achieved when at least athreshold number (i.e. 3, 22, 47, 93, 128, 431, etc.) or percentage(i.e. 49%, 53%, 68%, 72%, 95%, etc.) of features of the comparedpictures match. Similarly, substantial similarity can be achieved whenthe number or percentage of matching features of the compared picturesexceeds a threshold number (i.e. 3, 22, 47, 93, 128, 431, etc.) or athreshold percentage (i.e. 49%, 53%, 68%, 72%, 95%, etc.), in a furtherexample, substantial similarity can be achieved when all but a thresholdnumber or percentage of features of the compared pictures match. Suchthresholds can be defined by a user, by AIIM system administrator, orautomatically by the system based on experience, testing, inquiry,analysis, synthesis, and/or other techniques, knowledge, or input. Infurther aspects, Substantial Similarity Comparison 125 can utilize thetype of features for determining substantial similarity of pictures. Inone example, substantial similarity can be achieved when matches arefound with respect to edges, thereby tolerating mismatches in blobs. Inanother example, substantial similarity can be achieved when matches arefound with respect to more-substantive, larger, and/or other features,thereby tolerating mismatches in less-substantive, smaller, and/or otherfeatures. In further aspects, Substantial Similarity Comparison 125 canutilize the importance (i.e. as indicated by importance index [laterdescribed], etc.) of features for determining substantial similarity ofpictures. For example, substantial similarity can be achieved whenmatches are found with respect to more important features such as theabove described more-substantive, larger, and/or other features, therebytolerating mismatches in less important features such asless-substantive, smaller, and/or other features. In further aspects,Substantial Similarity Comparison 125 can omit some of the features fromthe comparison in determining substantial similarity of pictures. In oneexample, isolated features can be omitted from comparison. In anotherexample, less-substantive or smaller features can be omitted fromcomparison. In general, any feature can be omitted from comparison. Infurther aspects, Substantial Similarity Comparison 125 can focus onfeatures in certain regions of interest of the compared pictures. Forexample, substantial similarity can be achieved when matches are foundwith respect to features in regions comprising the face or parts (i.e.mouth, eyes, etc.) thereof, thereby tolerating mismatches in features ofregions comprising other body parts, the background, and/or otherregions. In further aspects, Substantial Similarity Comparison 125 candetect or recognize persons or objects in the compared pictures. Anyfeatures, functionalities, and embodiments of the previously describedPicture Recognizer 163 can be used in such detection or recognition.Once a person or object is detected in a picture, Substantial SimilarityComparison 125 may attempt to detect the person or object in thecompared picture. In one example, substantial similarity can be achievedwhen the compared pictures comprise the same person or object. Inanother example concerning Sub-streams of Digital Pictures 143,substantial similarity can be achieved when the compared Sub-streams ofDigital Pictures 143 comprise a detected person or object in at least athreshold number or percentage of their pictures. In further aspects,Substantial Similarity Comparison 125 may include identifying and/oranalyzing tiled and/or overlapping features, which can then be combined(i.e. similar to some process steps in convolutional neural networks,etc.) and compared to determine substantial similarity of pictures.

Substantial Similarity Comparison 125 can automatically adjust (i.e.increase or decrease) the strictness of the rules for determiningsubstantial similarity of pictures using features. In some aspects, suchadjustment in strictness can be done by Substantial SimilarityComparison 125 in response to determining that total equivalence matchhad not been found. In other aspects, an adjustment in strictness can bedone by Substantial Similarity Comparison 125 in response to determiningthat a substantially similar match had not been found. SubstantialSimilarity Comparison 125 can keep adjusting the strictness rules untila substantially similar match is found. All the rules or settings ofsubstantial similarity can be set, reset, or adjusted by SubstantialSimilarity Comparison 125 in response to another strictness leveldetermination. For example, Substantial Similarity Comparison 125 mayattempt to find a match in a certain percentage (i.e. 95%, etc.) offeatures from the compared pictures. If the comparison does not providea substantially similar match, Substantial Similarity Comparison 125 maydecide to decrease the strictness of the rules to find a substantiallysimilar match. In response, Substantial Similarity Comparison 125 mayattempt to find fewer matching features than in the previous attemptusing stricter rules. If the comparison still does not provide asubstantially similar match, Substantial Similarity Comparison 125 maydetermine to further decrease the strictness (i.e. down to a certainminimum strictness or threshold, etc.) by requiring fewer features tomatch, thereby further increasing a chance of finding a substantiallysimilar match.

Where a reference to a feature is used herein it should be understoodthat a portion of a feature or a collection of features can be usedinstead of or in addition to the feature. In one example, instead of orin addition to features, individual pixels that constitute the featurecan be compared. In another example, instead of or in addition tofeatures, collections of features can be compared. In a further example,levels of features where a feature on one level includes one or morefeatures from another level (i.e. prior level, etc.), can be compared.As such, any operations, rules, logic, and/or functions operating onfeatures similarly apply to any portion of a feature and/or anycollection of features. In general, whole features, portions of afeature, and/or collections of features, including any operationsthereon, can be combined to arrive at desired results. Some or all ofthe above-described rules, logic, and/or techniques can be utilizedalone or in combination with each other or with other rules, logic,and/or techniques. One of ordinary skill in art will recognize thatother techniques known in art for determining similarity of digitalpictures, streams thereof, and/or other data sequences that would be toovoluminous to describe are within the scope of this disclosure.

In other embodiments, in determining substantial similarity ofindividual pictures (i.e. pictures from the compared Sub-streams ofDigital Pictures 145, etc.), Substantial Similarity Comparison 125 cancompare pixels of one picture with pixels of another picture. In someaspects, total equivalence is found when all pixels of one picture matchall pixels of another picture. In other aspects, if total equivalence isnot found, Substantial Similarity Comparison 125 may attempt todetermine substantial similarity. In one example, substantial similaritycan be achieved when most of the pixels from the compared picturesmatch. In another example, substantial similarity can be achieved whenat least a threshold number (i.e. 449, 2219, 92229, 442990, 1000028,etc.) or percentage (i.e. 39%, 45%, 58%, 72%, 92%, etc.) of pixels fromthe compared pictures match. Similarly, substantial similarity can beachieved when the number or percentage of matching pixels from thecompared pictures exceeds a threshold number (i.e. 449, 2219, 92229,442990, 1000028, etc.) or a threshold percentage (i.e. 39%, 45%, 58%,72%, 92%, etc.). In a further example, substantial similarity can beachieved when all but a threshold number or percentage of pixels fromthe compared pictures match. Such thresholds can be defined by a user,by AIIM system administrator, or automatically by the system based onexperience, testing, inquiry, analysis, synthesis, and/or othertechniques, knowledge, or input. In further aspects, SubstantialSimilarity Comparison 125 can omit some of the pixels from thecomparison in determining substantial similarity of pictures, in oneexample, pixels composing the background or any insignificant contentcan be omitted from comparison. In general, any pixel can be omittedfrom comparison. In further aspects, Substantial Similarity Comparison125 can utilize collections or regions of pixels in determiningsubstantial similarity of pictures. A region may include any number ofpixels. For example, substantial similarity can be achieved when atleast a threshold number (i.e. 1, 2, 4, 9, 11, 28, etc.) or percentage(i.e. 19%, 32%, 55%, 62%, 94%, etc.) of regions of the compared picturesmatch or substantially match. Similarly, substantial similarity can beachieved when the number or percentage of matching regions of thecompared pictures exceeds a threshold number (i.e. 1, 2, 4, 9, 11, 28,etc.) or a threshold percentage (i.e. 19%, 32%, 55%, 62%, 94%, etc.). Infurther aspects, Substantial Similarity Comparison 125 can focus onpixels in certain regions of interest in determining substantialsimilarity of pictures. For example, substantial similarity can beachieved when matches are found with respect to pixels in regionscomprising the face or parts (i.e. mouth, eyes, etc.) thereof, therebytolerating mismatches in pixels of regions comprising other body parts,the background, and/or other regions.

The previously described functionality of Substantial SimilarityComparison 125 for automatically adjusting (i.e. increasing ordecreasing) the strictness of the rules for determining substantialsimilarity of pictures using features can similarly be used with pixels.In some aspects, such adjustment in strictness can be done bySubstantial Similarity Comparison 125 in response to determining thattotal equivalence match had not been found. In other aspects, anadjustment in strictness can be done by Substantial SimilarityComparison 125 in response to determining that a substantially similarmatch had not been found. For example, if the comparison does notprovide a substantially similar match using certain strictness,Substantial Similarity Comparison 125 may decide to decrease thestrictness (i.e. down to a certain minimum strictness or threshold,etc.) and attempt to find fewer matching pixels than in the previousattempt using stricter rules.

Where a reference to a pixel is used herein it should be understood thata collection (i.e. region, etc.) of pixels can be used instead of or inaddition to the pixel. For example, instead of or in addition to pixels,collections of pixels can be compared. As such, any operations, rules,logic, and/or functions operating on pixels similarly apply to anycollection of pixels. In general, pixels and/or collections of pixels,including any operations thereon, can be combined to arrive at desiredresults. Some or all of the above-described rules, logic, and/ortechniques can be utilized alone or in combination with each other orwith other rules, logic, and/or techniques. Any of the previouslydescribed features, functionalities, and embodiments of SubstantialSimilarity Comparison 125 for determining substantial similarity ofpictures using features can similarly be used for pixels, One ofordinary skill in art will recognize that other techniques known in artfor determining similarity of digital pictures; streams thereof, and/orother data sequences that would be too voluminous to describe are withinthe scope of this disclosure.

Other aspects or properties of digital pictures or pixels can be takeninto account by Substantial Similarity Comparison 125 in picturecomparisons. Examples of such aspects or properties include coloradjustment, size adjustment, transparency (i.e. alpha channel, etc.),use of mask, and/or others. In some implementations, as digital picturescan be captured by various picture taking equipment, in variousenvironments, and under various lighting conditions, SubstantialSimilarity Comparison 125 can adjust lighting or color of pixels orotherwise manipulate pixels before or during comparison. Lighting orcolor adjustment (also referred to as gray balance, neutral balance,white balance, etc.) may generally include manipulating or rebalancingthe intensities of the colors (i.e. red, green, and/or blue if RGB colormodel is used, etc.) of one or more pixels. For example, SubstantialSimilarity Comparison 125 can adjust lighting or color of all pixels ofone picture to make it more comparable to another picture. SubstantialSimilarity Comparison 125 can also incrementally adjust the pixels suchas increasing or decreasing the red, green, and/or blue pixel values bya certain amount in each cycle of comparisons in order to find asubstantially similar match at one of the incremental adjustment levels.Any of the publically available, custom, or other lighting or coloradjustment techniques or programs can be utilized such as color filters,color balancing, color correction, and/or others. In otherimplementations, Substantial Similarity Comparison 125 can resize orotherwise transform a digital picture before or during comparison, Suchresizing or transformation may include increasing or decreasing thenumber of pixels. For example, Substantial Similarity Comparison 125 canincrease or decrease the size of a picture proportionally (i.e. increaseor decrease length and/or width keeping aspect ratio constant, etc.) toequate its size with the size of another picture. Substantial SimilarityComparison 125 can also incrementally resize a picture such asincreasing or decreasing the size of the picture proportionally by acertain amount in each cycle of comparisons in order to find asubstantially similar match at one of the incremental sizes. Any of thepublically available, custom, or other digital picture resizingtechniques or programs can be utilized such as nearest-neighborinterpolation, bilinear interpolation, bicubic interpolation, and/orothers. In further implementations, in digital pictures comprisingtransparency features or functionalities, Substantial SimilarityComparison 125 can utilize a threshold for acceptable number orpercentage transparency difference similar to the below-describedthreshold for the acceptable color difference. Alternatively,transparency can be applied to one or more pixels of a picture and colordifference may then be determined between compared pixels taking intoaccount the transparency related color effect. Alternatively,transparent pixels can be excluded from comparison. In furtherimplementations, certain regions or subsets of pixels can be ignoredduring comparison using a mask to define the region or subset of pixelsexcluded from comparison. In general, any region or subset of a picturedetermined to contain no content of interest can be excluded fromcomparison using a mask, Examples of such regions or subsets includebackground, transparent or partially transparent regions, regionscomprising insignificant content, or any arbitrary region or subset.Substantial Similarity Comparison 125 can perform any otherpre-processing or manipulation of digital pictures or pixels before orduring comparison.

In any of the comparisons involving digital pictures or pixels,Substantial Similarity Comparison 125 can utilize a threshold foracceptable number or percentage difference in determining a match foreach compared pixel. A pixel in a digital picture can be encoded usingvarious techniques such as RGB (i.e. red, green, blue), CMYK (i.e. cyan,magenta, yellow, and key [black]), binary value, hexadecimal value,numerical value, and/or others. For instance, in RGB color scheme, eachof red, green, and blue colors is encoded with a value 0-255 or itsbinary equivalent. In one example, a threshold for acceptable difference(i.e. absolute difference, etc.) can be set at 10 for each of the threecolors. Therefore, a pixel encoded as R130, G240, B50 matches or issufficiently similar to a compared pixel encoded as R135, G231, B57because the differences in all three colors fall within the acceptabledifference threshold (i.e. 10 in this example, etc.). Furthermore, apixel encoded as R130, G240, B50 does not match or is not sufficientlysimilar to a compared pixel encoded as R143, G231, B57 because thedifference in red value falls outside the acceptable differencethreshold. Any other number threshold can be used such as 1, 3, 8, 15,23, 77, 132, 197, 243, and/or others. A threshold for acceptablepercentage difference can similarly be utilized such as 0.12%, 2%, 7%,14%, 23%, 36%, 65%, and/or others. In some aspects, a threshold foracceptable number or percentage difference in red, green, and blue canbe set to be different for each color. A similar differencedetermination can be utilized in pixels encoded in any other colorscheme. The aforementioned thresholds can be defined by a user, by AIIMsystem administrator, or automatically by the system based onexperience, testing, inquiry, analysis, synthesis, or other techniques,knowledge, or input.

In some embodiments, the previously described Extra Info 250 can be usedto enhance any of the aforementioned substantial similaritydeterminations. Extra Info 250 may include any contextual or otherinformation that can be useful in determining similarity between anycompared elements. In one example, Extra info 250 includes time stamp(i.e. time information, etc.) of a Sub-stream of Digital Pictures 145,Sub-stream of Digital Sound Samples 155, Conversational Activity 210,Round of Conversational Exchange 200, and/or other element. In anotherexample, Extra info 250 includes location (i.e. location information,etc.) of the Conversation Participant 50 while engaged in aconversation. In a further example, Extra Info 250 includes objects orenvironments (i.e. observed information, etc.) recognized fromSub-stream of Digital Pictures 145 and/or Sub-stream of Digital SoundSamples 155. In some aspects, in determining substantial similarity ofSub-streams of Digital Pictures 145, Substantial Similarity Comparison125 can compare one or more Extra Info 250 (i.e. time stamp, locationinformation, observed information, etc.) in addition to or instead ofcomparing pictures (i.e. frames, etc.), features, pixels, and/or otherelements. Extra info 250 can be set to be less, equally, or moreimportant (i.e. as indicated by importance index [later described],etc.) than pictures, features, pixels, and/or other elements in thecomparison. In other aspects, in determining substantial similarity ofSub-streams of Digital Sound Samples 155, Substantial SimilarityComparison 125 can compare one or more Extra Info 250 in addition to orinstead of comparing words, features, sound samples, and/or otherelements. Extra Info 250 can be set to be less, equally, or moreimportant than words, features, sound samples, and/or other elements inthe comparison. In further aspects, in determining substantialsimilarity of Conversational Activities 210, Substantial SimilarityComparison 125 can compare one or more Extra Info 250 in addition to orinstead of comparing Sub-streams of Digital Pictures 145, Sub-streams ofDigital Sound Samples 155, and/or other elements, Extra Info 250 can beset to be less, equally, or more important than any Sub-stream ofDigital Pictures 145, Sub-stream of Digital Sound Samples 155, and/orother elements in the comparison. In further aspects, in determiningsubstantial similarity of Rounds of Conversational Exchange 200,Substantial Similarity Comparison 125 can compare one or more Extra Info250 in addition to or instead of comparing Conversational Activities 210and/or other elements. Extra Info 250 can be set to be less, equally, ormore important than any Conversational Activity 210 and/or otherelements in the comparison.

In some embodiments, an importance index (not shown) or other importanceranking technique can be used in any of the previously describedcomparisons or other processing involving elements of differentimportance. Importance index indicates importance of the element to orwith which the index is assigned or associated. For example, importanceindex may indicate importance of Round of Conversational Exchange 200,Conversational Activity 210, Sub-stream of Digital Pictures 145,Sub-stream of Digital Sound Samples 155, word, feature, and/or otherelement to or with which the index is assigned or associated. In someaspects, importance index on a scale from 0 to 1 can be utilized,although, any other range can also be utilized. Importance index can bestored in or associated with the element to which the index pertains.Association of importance indexes can be implemented using a table whereone column comprises elements and another column comprises theirassociated importance indexes, for example. Importance indexes ofvarious elements can be defined by a user, by AIIM system administrator,or automatically by the system based on experience, testing, inquiry,analysis, synthesis, or other techniques, knowledge, or input. In oneexample, a higher Importance index can be assigned to speakingConversational Activities 210. In another example, a higher Importanceindex can be assigned to Extra info 250. In a further example, a higherimportance index can be assigned to front-most words recognized from aSub-stream of Digital Sound Samples 155. Any importance index can beassigned to or associated with any element described herein. Anyimportance ranking technique can be utilized as or instead of importanceindex in alternate embodiments.

In some embodiments, Substantial Similarity Comparison 125 may generatea similarity index (not shown) for any compared elements. Similarityindex indicates how well an element is matched with another element. Forexample, similarity index indicates how well a Round of ConversationalExchange 200, Conversational Activity 210, Sub-stream of DigitalPictures 145, Sub-stream of Digital Sound Samples 155, word, feature,and/or other element is matched with a compared element. In someaspects, similarity index on a scale from 0 to 1 can be utilized,although, any other range can also be utilized. Similarity index can begenerated by Substantial Similarity Comparison 125 whether substantialsimilarity between the compared elements is achieved or not. In oneexample, similarity index can be determined for a Sub-stream of DigitalSound Samples 155 based on a ratio/percentage of matched words and/orExtra info 250 relative to the number of all words and/or Extra Info 250in the Sub-stream of Digital Sound Samples 155. Specifically, similarityindex of 0.46 is determined if 46% of words and/or Extra info 250 match.In some designs, importance (i.e. as indicated by importance index,etc.) of one or more words and/or Extra Info 250 can be included in thecalculation of a weighted similarity index. Similar determination ofsimilarity index can be implemented with features, sound samples, and/orother elements of Sub-stream of Digital Sound Samples 155. In a furtherexample, similarity index can be determined for a Sub-stream of DigitalPictures 145 based on a ratio/percentage of matched pictures (i.e.frames, etc.) and/or Extra Info 250 relative to the number of allpictures (i.e. frames, etc.) and/or Extra Info 250 in the Sub-stream ofDigital Pictures 145. Specifically, similarity index of 0.93 isdetermined if 93% of pictures (i.e. frames, etc.) and/or Extra info 250match or substantially match. In some designs, importance (i.e. asindicated by importance index, etc.) of one or more pictures (i.e.frames, etc.) and/or Extra Info 250 can be included in the calculationof a weighted similarity index. Similar determination of similarityindex can be implemented with features, pixels, and/or other elements ofSub-stream of Digital Pictures 145. In another example, similarity indexcan be determined for a Conversational Activity 210 based onsimilarities or similarity indexes of Sub-streams of Digital Pictures145, Sub-streams of Digital Sound Samples 155, and/or Extra Info 250 inthe compared Conversational Activities 210. In some designs, an averageor weighted average of similarities or similarity indexes of Sub-streamsof Digital Pictures 145, Sub-streams of Digital Sound Samples 155,and/or Extra info 250 can be used to calculate a weighted similarityindex for a Conversational Activity 210. In another example, similarityindex can be determined for a Round of Conversational Exchange 120 basedon similarities or similarity indexes of Conversational Activities 210and/or Extra Info 250 in the compared Rounds of Conversational Exchange120. In some designs, an average or weighted average of similarities orsimilarity indexes of Conversational Activities 210 and/or Extra Info250 can be used in calculating a weighted similarity index for a Roundof Conversational Exchange 120. Any combination of the aforementionedsimilarity index determinations or calculations can be utilized inalternate embodiments. Any similarity ranking technique can be utilizedto determine or calculate similarity index in alternate embodiments.

Referring to FIG. 14, an exemplary embodiment of learning Rounds ofConversational Exchange 200 using Neural Network 130 a comprisingshortcut Connections 853 is illustrated. In some aspects, Rounds ofConversational Exchange 200 in one Layer 854 can be connected withRounds of Conversational Exchange 200 in any Layer 854, not only in asuccessive Layer 854, thereby creating shortcuts (i.e. shortcutConnections 853, etc.) through Neural Network 130 a, Creating a shortcutConnection 853 can be implemented by performing Substantial SimilarityComparisons 125 of a Round of Conversational Exchange 200 from KnowledgeStructuring Unit 110 with Rounds of Conversational Exchange 200 in anyLayer 854 when applying (i.e. storing, copying, etc.) the Round ofConversational Exchange 200 from Knowledge Structuring Unit 110 ontoNeural Network 130 a. Once created, shortcut Connections 853 enable awider variety of Rounds of Conversational Exchange 200 to be consideredwhen selecting a path through Neural Network 130 a, In some embodiments,Knowledge Structuring Unit 110 creates or generates Rounds ofConversational Exchange 200 and the system applies them onto NeuralNetwork 130 a, thereby implementing learning of Rounds of ConversationalExchange 200. The system can perform Substantial Similarity Comparisons125 of a Round of Conversational Exchange 200 from Knowledge StructuringUnit 110 with Rounds of Conversational Exchange 200 in a correspondingand/or other Layers 854 of Neural Network 130 a. If a substantiallysimilar Round of Conversational Exchange 200 is not found in thecorresponding or other Layers 854 of Neural Network 130 a, the systemmay insert (i.e. copy, store, etc.) the Round of Conversational Exchange200 from Knowledge Structuring Unit 110 into the corresponding (oranother) Layer 854 of Neural Network 130 a, and create a Connection 853to the inserted Round of Conversational Exchange 200 from a prior Roundof Conversational Exchange 200 including assigning an occurrence countto the new Connection 853, calculating a weight of the new Connection853, and updating any other Connections 853 originating from the priorRound of Conversational Exchange 200. On the other hand, if asubstantially similar Round of Conversational Exchange 200 is found inthe corresponding or other Layers 854 of Neural Network 130 a, thesystem may update occurrence count and weight of Connection 853 to thatRound of Conversational Exchange 200 from a prior Round ofConversational Exchange 200, and update any other Connections 853originating from the prior Round of Conversational Exchange 200. Any ofthe previously described and/or other techniques for comparing,inserting, updating, and/or other operations on Rounds of ConversationalExchange 200, Connections 853, Layers 854, and/or other elements cansimilarly be utilized in Neural Network 130 a that comprises shortcutConnections 853.

Referring to FIG. 15, an exemplary embodiment of learning Rounds ofConversational Exchange 200 using Graph 130 b is illustrated. In someaspects, any Round of Conversational Exchange 200 can be connected withany other Round of Conversational Exchange 200 in Graph 130 b. In otheraspects, any Round of Conversational Exchange 200 can be connected withitself and/or any other Round of Conversational Exchange 200 in Graph130 b. In some embodiments, Knowledge Structuring Unit 110 creates orgenerates Rounds of Conversational Exchange 200 and the system appliesthem onto Graph 130 b, thereby implementing learning of Rounds ofConversational Exchange 200. The system can perform SubstantialSimilarity Comparisons 125 of a Round of Conversational Exchange 200from Knowledge Structuring Unit 110 with Rounds of ConversationalExchange 200 in Graph 130 b. If a substantially similar Round ofConversational Exchange 200 is not found in Graph 130 b, the system mayinsert (i.e. copy, store, etc.) the Round of Conversational Exchange 200from Knowledge Structuring Unit 110 into Graph 130 b, and create aConnection 853 to the inserted Round of Conversational Exchange 200 froma prior Round of Conversational Exchange 200 including assigning anoccurrence count to the new Connection 853, calculating a weight of thenew Connection 853, and updating any other Connections 853 originatingfrom the prior Round of Conversational Exchange 200. On the other hand,if a substantially similar Round of Conversational Exchange 200 is foundin Graph 130 b, the system may update occurrence count and weight ofConnection 853 to that Round of Conversational Exchange 200 from a priorRound of Conversational Exchange 200, and update any other Connections853 originating from the prior Round of Conversational Exchange 200. Anyof the previously described and/or other techniques for comparing,inserting, updating, and/or other operations on Rounds of ConversationalExchange 200, Connections 853, and/or other elements can similarly beutilized in Graph 130 b.

For example, the system can perform Substantial Similarity Comparisons125 of Round of Conversational Exchange 200 aa from KnowledgeStructuring Unit 110 with Rounds of Conversational Exchange 200 in Graph130 b. In the case that a substantially similar match is not found, thesystem may insert Round of Conversational Exchange 200 ta into Graph 130b and copy Round of Conversational Exchange 200 aa into the insertedRound of Conversational Exchange 200 ta. The system can then performSubstantial Similarity Comparisons 125 of Round of ConversationalExchange 200 ab from Knowledge Structuring Unit 110 with Rounds ofConversational Exchange 200 in Graph 130 b. In the case that asubstantially similar match is found between Round of ConversationalExchange 200 ab and Round of Conversational Exchange 200 tb, the systemmay create Connection 853 t 1 between Round of Conversational Exchange200 ta and Round of Conversational Exchange 200 tb with occurrence countof 1 and weight of 1. The system can then perform Substantial SimilarityComparisons 125 of Round of Conversational Exchange 200 ac fromKnowledge Structuring Unit 110 with Rounds of Conversational Exchange200 in Graph 130 b. In the case that a substantially similar match isfound between Round of Conversational Exchange 200 ac and Round ofConversational Exchange 200 tc, the system may update occurrence countand weight of Connection 853 t 2 between Round of ConversationalExchange 200 tb and Round of Conversational Exchange 200 tc, and updateweights of other outgoing Connections 853 (one in this example)originating from Round of Conversational Exchange 200 tb as previouslydescribed. The system can then perform Substantial SimilarityComparisons 125 of Round of Conversational Exchange 200 ad fromKnowledge Structuring Unit 110 with Rounds of Conversational Exchange200 in Graph 130 b. In the case that a substantially similar match isnot found, the system may insert Round of Conversational Exchange 200 tdinto Graph 130 b and copy Round of Conversational Exchange 200 ad intothe inserted Round of Conversational Exchange 200 td. The system mayalso create Connection 853 t 3 between Round of Conversational Exchange200 tc and Round of Conversational Exchange 200 td with occurrence countof 1 and weight calculated based on the occurrence count as previouslydescribed. The system may also update weights of other outgoingConnections 853 (one in this example) originating from Round ofConversational Exchange 200 tc as previously described. The system canthen perform Substantial Similarity Comparisons 125 of Round ofConversational Exchange 200 ae from Knowledge Structuring Unit 110 withRounds of Conversational Exchange 200 in Graph 130 b. In the case that asubstantially similar match is not found; the system may insert Round ofConversational Exchange 200 te into Graph 130 b and copy Round ofConversational Exchange 200 ae into the inserted Round of ConversationalExchange 200 te. The system may also create Connection 853 t 4 betweenRound of Conversational Exchange 200 td and Round of ConversationalExchange 200 te with occurrence count of 1 and weight of 1, Applying anyadditional Rounds of Conversational Exchange 200 from KnowledgeStructuring Unit 110 onto Graph 130 b follows similar logic or processas the above-described.

Referring to FIG. 16, an exemplary embodiment of learning Rounds ofConversational Exchange 200 using Collection of Sequences 130 c isillustrated. Collection of Sequences 130 c comprises the functionalityfor storing one or more Sequences 133. Sequence 133 comprises thefunctionality for storing multiple Rounds of Conversational Exchange 200and/or other elements. Connections 853 can be used to link Rounds ofConversational Exchange 200 or can be optionally omitted in someimplementations of Sequence 133. In some aspects, each Sequence 133 in aCollection of Sequences 130 c may include Rounds of ConversationalExchange 200 of an entire conversation. For example, KnowledgeStructuring Unit 110 creates or generates Rounds of ConversationalExchange 200 and the system applies them onto Collection of Sequences130 c, thereby implementing learning of Rounds of ConversationalExchange 200, The system can perform Substantial Similarity Comparisons125 of Rounds of Conversational Exchange 200 from Knowledge StructuringUnit 110 with corresponding Rounds of Conversational Exchange 200 inSequences 133 stored in Collection of Sequences 130 c to find a Sequence133 comprising Rounds of Conversational Exchange 200 that aresubstantially similar to the Rounds of Conversational Exchange 200 fromKnowledge Structuring Unit 110. If Sequence 133 comprising suchsubstantially similar Rounds of Conversational Exchange 200 is not foundin Collection of Sequences 130 c, the system may create a new Sequence133 comprising the Rounds of Conversational Exchange 200 from KnowledgeStructuring Unit 110 and insert (i.e. copy, store, etc.) the newSequence 133 into Collection of Sequences 130 c. On the other hand, ifSequence 133 comprising substantially similar Rounds of ConversationalExchange 200 is found in Collection of Sequences 130 c, the system mayoptionally omit inserting the Rounds of Conversational Exchange 200 fromKnowledge Structuring Unit 110 into Collection of Sequences 130 c asinserting a similar Sequence 133 may not add much or any additionalknowledge. This approach can save storage resources and limit the numberof Rounds of Conversational Exchange 200 that may later need to beprocessed or compared. In other aspects, each Sequence 133 in aCollection of Sequences 130 c may include Rounds of ConversationalExchange 200 of a part of a conversation. A similar learning process asthe above described can be utilized in such implementations. In furtheraspects, one or more long Sequences 133 each including Rounds ofConversational Exchange 200 of multiple conversations can be utilized.In one example, Rounds of Conversational Exchange 200 of allconversations can be stored in a single long Sequence 133 in which caseCollection of Sequences 130 c as a separate element can be omitted. Inanother example, Rounds of Conversational Exchange 200 of multipleconversations can be included in a plurality of long Sequences 133 suchas daily, weekly, monthly, yearly, or other periodic or other Sequences133. Substantial Similarity Comparisons 125 can be performed bytraversing the one or more long Sequences 133 to find a match orsubstantially similar match. For example, the system can performSubstantial Similarity Comparisons 125 of Rounds of ConversationalExchange 200 from Knowledge Structuring Unit 110 with correspondingRounds of Conversational Exchange 200 in subsequences of a long Sequence133 in incremental or other traversing pattern to find a subsequencecomprising Rounds of Conversational Exchange 200 that are substantiallysimilar to the Rounds of Conversational Exchange 200 from KnowledgeStructuring Unit 110. The incremental traversing pattern may start fromone end of a long Sequence 133 and move the comparison subsequence up ordown one (i.e. or any amount, etc.) incremental Round of ConversationalExchange 200 at a time. Other traversing patterns or methods can beemployed such as starting from the middle of the Sequence 133 andsubdividing the resulting sub-sequences in a recursive pattern, or anyother traversing pattern or method. If a subsequence comprisingsubstantially similar Rounds of Conversational Exchange 200 is not foundin the long Sequence 133, Knowledge Structuring Unit 110 may concatenateor append the Rounds of Conversational Exchange 200 from KnowledgeStructuring Unit 110 to the long Sequence 133. In further aspects, aRound of Conversational Exchange 200 in a single Sequence 133 can beconnected not only with a next Round of Conversational Exchange 200 inthe Sequence 133, but also with any other Round of ConversationalExchange 200, thereby creating alternate routes or shortcuts through theSequence 133. Any number of Connections 853 connecting any Rounds ofConversational Exchange 200 can be utilized. For example, KnowledgeStructuring Unit 110 creates or generates Rounds of ConversationalExchange 200 and the system applies them onto a single Sequence 133,thereby implementing learning of Rounds of Conversational Exchange 200.The system can perform Substantial Similarity Comparisons 125 of a Roundof Conversational Exchange 200 from Knowledge Structuring Unit 110 withRounds of Conversational Exchange 200 in Sequence 133, If asubstantially similar Round of Conversational Exchange 200 is not foundin Sequence 133, the system may concatenate or append the Round ofConversational Exchange 200 from Knowledge Structuring Unit 110 to theSequence 133 and create (i.e. including assigning occurrence count andweight, etc.) a Connection 853 to that Round of Conversational Exchange200 from a prior Round of Conversational Exchange 200. On the otherhand, if a substantially similar Round of Conversational Exchange 200 isfound in Sequence 133, the system may create (i.e. including assigningoccurrence count and weight, etc.) a Connection 853 to that Round ofConversational Exchange 200 from a prior Round of ConversationalExchange 200 if the Connection 853 did not exist. Otherwise, the systemmay update (i.e. update occurrence count and weight, etc.) theConnection 853 if the Connection 853 existed. Any of the previouslydescribed and/or other techniques for comparing, inserting, updating,and/or other operations on Rounds of Conversational Exchange 200,Connections 853, and/or other elements can similarly be utilized inSequences 133.

Any of the previously described arrangements of Rounds of ConversationalExchange 200 such as Neural Network 130 a, Graph 130 b, Collection ofSequences 130 c, Sequence 133, and/or others can be used alone or incombination with each other or with other elements in alternateembodiments. In one example, a path in Neural Network 130 a or Graph 130b may include its own separate sequence of Rounds of ConversationalExchange 200 that are not interconnected with Rounds of ConversationalExchange 200 in other paths. In another example, a portion of a path inNeural Network 130 a or Graph 130 b may include a sequence of Rounds ofConversational Exchange 200 interconnected with Rounds of ConversationalExchange 200 in other paths, whereas, another portion of the path mayinclude its own separate sequence of Rounds of Conversational Exchange200 that are not interconnected with Rounds of Conversational Exchange200 in other paths. Any other combinations or arrangements of Rounds ofConversational Exchange 200 can be implemented.

Referring now to some embodiments of System for Learning AIIMs 100,System for Learning AIIMs 100 can be implemented to learn only speech orsounds of Conversation Participants 50 in which case the elements forprocessing pictures can be omitted. Such System for Learning AIIMs 100that learns only speech or sounds can be utilized in any situation wherea Picture-capturing Device 140 may not be available or where visualinput is undesirable. Examples of such situations include situationswhere people prefer not to be visually recorded, situations involvingverbal communication systems with no visual input (i.e. telephonesystems, etc.), and/or others.

Referring to some embodiments of System for Learning AIIMs 100, one ormore Conversation Participants 50 may be non-human ConversationParticipants 50. A non-human Conversation Participant 50 may include anydevice, apparatus, or system comprising conversational functionalitiessuch as a humanoid or other robot, conversation participant simulated ona computing device, and/or others. System for Learning AIIMs 100 canlearn conversations among a human Conversation Participant 50 and anon-human Conversation Participant 50. As the human ConversationParticipant 50 and the non-human Conversation Participant 50 exchangeverbal and visual expressions or communication in a conversation, Systemfor Learning AIIMs 100 may capture and learn these conversationalexchanges as previously described. In some aspects, a non-humanConversation Participant 50 may be configured to guide a conversationwith a human Conversation Participant 50 to enhance the learningeffectiveness of System for Learning AIIMs 100. For example, thenon-human Conversation Participant 50 may perform specificconversational activities (i.e. ask specific questions, make specificfacial expressions, etc.) to cause desired conversational activities(i.e. specific statements, specific facial expressions, etc.) to beperformed by the human Conversation Participant 50. This way, System forLearning AIIMs 100 can learn or be trained to learn targeted ordesirable verbal and visual expressions or communication of the humanConversation Participant 50 in a controlled process. A similar processcan be implemented with a human Conversation Participant 50 who isinstructed to guide a conversation with another human ConversationParticipant 50.

Referring to FIG. 17, the illustration shows an embodiment of a method6100 for learning AIIMs. The method can be used on a computing device orsystem to enable learning of conversations among two or moreconversation participants. The computing device or system may include adedicated device, a user device (i.e. User Device 80, etc.), a server(i.e. Server 90, etc.), a host device (i.e. Host Device 98, etc.) or anembedded element thereof, and/or others. Method 6100 may include anyaction or operation of any of the disclosed methods such as method 6200and/or others. Other additional steps, actions, or operations can beincluded as needed, or some of the disclosed ones can be optionallyomitted, or a different combination or order thereof can be implementedin alternate embodiments of method 6100.

At step 6105, a stream of digital pictures of a first conversationparticipant is captured. A stream of digital pictures (i.e. Stream ofDigital Pictures 143, etc.) may include a conversation participant's(i.e. Conversation Participant's 50, etc.) visual expressions orcommunication. In some embodiments, a stream of digital picturescomprises any type or form of digital motion picture such as MPEG, AVI,FLV, MOV, RM, SWF, WMV, DivX, and/or other digitally encoded motionpicture. In other embodiments, a stream of digital pictures comprisesany type or form of digital pictures such as digital bitmaps, JPEGpictures, GIF pictures, TIFF pictures, PDF pictures, and/or otherdigitally encoded pictures. In yet other embodiments, a stream ofdigital pictures comprises any computer-generated pictures such as viewsof a 3D game, 3D application, or CAD/CAM application captured orrendered as a stream of digital pictures. In further embodiments, astream of digital pictures comprises any application or process that cangenerate a stream of digital pictures, or other visual content. A streamof digital pictures comprising a conversation participant's visualexpressions or communication can be captured by a picture-capturingdevice (i.e. Picture-capturing Device 140, etc.) such as a motion orstill picture camera, or other picture capturing device. In someaspects, a picture-capturing device may be part of a device dedicated toimplementing AIIM learning functionalities. In other aspects, apicture-capturing device may be part of a user device that is connectedvia a network to a server implementing AIIM learning functionalities. Infurther aspects, a picture-capturing device may be part of a host devicewhose embedded element implements AIIM learning functionalities.Picture-capturing device may be provided in any other device, system, orconfiguration. In some aspects, a plurality of picture-capturing devicescan be utilized each dedicated to capturing visual expressions orcommunication of a single conversation participant. In other aspects, asingle picture-capturing device may capture visual expressions orcommunication of a plurality of conversation participants. Capturingcomprises any action or operation by or for a Picture-capturing Device140, Stream of Digital Pictures 143, and/or other disclosed elements.

At step 6110, a stream of digital sound samples of the firstconversation participant is captured. A stream of digital sound samples(i.e. Stream of Digital Sound Samples 153, etc.) may include aconversation participant's verbal expressions or communication. In someembodiments, a stream of digital sound samples comprises any type orform of digital sound such as WAV, WMA, AIFF, MP3, RA, OGG, and/or otherdigitally encoded sound. In other embodiments, a stream of digital soundsamples comprises any computer-generated stream of digital sound samplessuch as synthesized sound. In further embodiments, a stream of digitalsound samples comprises any application or process that can generate astream of digital sound samples, or other audio content. In somedesigns, stream of digital sound samples is captured simultaneously withthe aforementioned stream of digital pictures, and vice versa. Stream ofdigital sound samples may therefore be associated with or correspond toa stream of digital pictures. A stream of digital sound samplescomprising a conversation participant's verbal expressions orcommunication can be captured by a sound-capturing device (i.e.Sound-capturing Device 150, etc.) such as a microphone or other soundcapturing device. In some aspects, a sound-capturing device may be partof a device dedicated to implementing AIIM learning functionalities. Inother aspects, a sound-capturing device may be part of a user devicethat is connected via a network to a server implementing AIIM learningfunctionalities. In further aspects, a sound-capturing device may bepart of a host device whose embedded element implements AIIM learningfunctionalities. Sound-capturing device may be provided in any otherdevice, system, or configuration. In some aspects, a plurality ofsound-capturing devices can be utilized each dedicated to capturingverbal expressions or communication of a single conversationparticipant. In other aspects, a single sound-capturing device maycapture verbal expressions or communication of a plurality ofconversation participants. Capturing comprises any action or operationby or for a Sound-capturing Device 150, Stream of Digital Sound Samples153, and/or other disclosed elements.

At step 6115, a stream of digital pictures of a second conversationparticipant is captured. Step 6115 may include any action or operationdescribed in Step 6105 as applicable. Step 6115 may be performedconcurrently with Step 6105 and/or 6110.

At step 6120, a stream of digital sound samples of the secondconversation participant is captured. Step 6120 may include any actionor operation described in Step 6110 as applicable. Step 6120 may beperformed concurrently with Step 6105 and/or 6110.

At step 6125, the first conversation participant's first conversationalactivity is detected from at least one of the stream of digital picturesof the first conversation participant or the stream of digital soundsamples of the first conversation participant. Examples ofconversational activities include speaking, silent facial expressions,silent body movements, motionless silence, absence from theconversation, and/or others, Detecting conversational activities of aconversation participant may include processing either or both a streamof digital pictures comprising the conversation participant's visualexpressions or communication and/or a stream of digital sound samplescomprising the conversation participant's verbal expressions orcommunication. A stream of digital pictures may include visualexpressions or communication of a single conversation participant or aplurality of conversation participants. Similarly, a stream of digitalsound samples may include verbal expressions or communication of asingle conversation participant or a plurality of conversationparticipants. In one example, a conversation participant's speakingactivity can be detected by recognizing the conversation participant'sspeech in a stream of digital sound samples. Specifically, for instance,a beginning of a conversation participant's speaking activity can bedetermined by recognizing the conversation participant's speech in astream of digital sound samples after a threshold period of silence(i.e. no speech or sound, etc.). Further, an end of a conversationparticipant's speaking activity can be determined by recognizing athreshold period of silence in a stream of digital sound samples afterthe conversation participant's speech. In another example, aconversation participant's silent facial expressions activity can bedetected by recognizing the conversation participant's facialexpressions (i.e. smiling, lifting eyebrows, etc.) in a stream ofdigital pictures and by recognizing the conversation participant'ssilence (i.e. no speech or sound, etc.) in a stream of digital soundsamples. In another example, a conversation participant's silent bodymovements activity can be detected by recognizing the conversationparticipant's body movements (i.e. nodding head, shaking head, shruggingshoulders, pointing finger, pointing fist, etc.) in a stream of digitalpictures and by recognizing the conversation participant's silence (i.e.no speech or sound, etc.) in a stream of digital sound samples. In afurther example, a conversation participant's motionless silenceactivity can be detected by recognizing no or marginal motion (i.e. nofacial change, no body movement, etc.) of the conversation participantin a stream of digital pictures and by recognizing the conversationparticipant's silence (i.e. no speech or sound, etc.) in a stream ofdigital sound samples. For instance, marginal motion of a conversationparticipant may include comparing one picture of a stream of digitalpictures with another (i.e. subsequent, etc.) picture of the stream ofdigital pictures and determining that a number or percentage differencebetween regions of the two pictures comprising the conversationparticipant does not exceed a threshold. In a further example, aconversation participant's absence from the conversation activity can bedetected by recognizing the conversation participant's absence (i.e.conversation participant missing from the field of view, etc.) in astream of digital pictures and/or by recognizing the conversationparticipant's silence (i.e. no speech or sound, etc.) in a stream ofdigital sound samples. In some aspects, detecting a conversationalactivity may include comparing collections of sound samples of a streamof digital sound samples with collections of known sound samples. Infurther aspects, detecting a conversational activity may includecomparing features of a stream of digital sound samples with features ofknown sounds. For example, acoustic features can be used forclassification of non-verbal vocal outbursts such as laughter or sighswhereas linguistic features can be used to transcribe the linguisticmessage such as words, phrases, or sentences. In other aspects,detecting a conversational activity may include comparing regions ofpixels from one or more pictures (i.e. frames, etc.) of a stream ofdigital pictures with collections of pixels comprising known persons,objects, and/or their activities. In other aspects, detecting aconversational activity may include comparing features (i.e. lines,edges, ridges, corners, blobs, regions, etc.) from one or more pictures(i.e. frames, etc.) of a stream of digital pictures with features ofknown persons, objects, and/or their activities. In one example, facialrecognition involves identifying and/or analyzing facial features suchas the relative position, size, and/or shape of the eyes, nose,cheekbones, jaw, etc., which may then be used to search for pictureswith matching features. In further aspects, detecting any newconversational activity may mark an end to a previously detectedconversational activity. In some designs, detecting a conversationalactivity of a conversation participant may include recognizing theconversation participant's visual expressions or communication in a partof a conversation. Similarly, detecting a conversational activity of aconversation participant may include recognizing the conversationparticipant's verbal expressions or communication in a part of aconversation. In other designs, detecting a conversational activity of aconversation participant may include identifying a sub-stream of digitalpictures (i.e. Sub-stream of Digital Pictures 145, etc.) in a stream ofdigital pictures, the sub-stream of digital pictures comprising theconversation participant's visual expressions or communication in a partof a conversation. Similarly, detecting a conversational activity of aconversation participant may include identifying a sub-stream of digitalsound samples (i.e. Sub-stream of Digital Sound Samples 155, etc.) in astream of digital sound samples, the sub-stream of digital sound samplescomprising the conversation participant's verbal expressions orcommunication in a part of a conversation. Detecting a conversationalactivity of a conversation participant may also include creating orgenerating a recording or data structure of the conversational activity(i.e. Conversational Activity 210, also sometimes referred to simply asconversational activity, etc.) that comprises one or more sub-streams ofdigital pictures, one or more sub-streams of digital sound samples,and/or other data (i.e. Extra info 250, etc.). In further designs,detecting a conversational activity of a conversation participant mayinclude extracting or filtering persons and/or objects of interest (i.e.conversation participant's face, etc.) from a stream of digital picturesor sub-stream of digital pictures. Similarly, detecting a conversationalactivity of a conversation participant may include extracting orfiltering speech and/or sounds of interest (i.e. conversationparticipant's speech and/or sounds, etc.) from a stream of digital soundsamples or sub-stream of digital sound samples. Such extractions orfiltering can optionally be performed as part of another step or intheir own separate steps in alternate embodiments. Detecting comprisesany action or operation by or for an Activity Detector 160, PictureRecognizer 163, Speech/Sound Recognizer 165, Sub-stream of DigitalPictures 145, Sub-stream of Digital Sound Samples 155, ConversationalActivity 210, and/or other disclosed elements.

At step 6130, the second conversation participant's first conversationalactivity is detected from at least one of the stream of digital picturesof the second conversation participant or the stream of digital soundsamples of the second conversation participant. Step 6130 may includeany action or operation described in Step 6125 as applicable.

At step 6135, a first round of conversational exchange including arecording of the first conversation participant's first conversationalactivity and a recording of the second conversation participant's firstconversational activity is generated. A round of conversational exchange(i.e. Round of Conversational Exchange 200, etc.) may include one ormore recordings of one conversation participant's conversationalactivities (i.e. Conversational Activities 210, etc.) and one or morerecordings of another conversation participant's conversationalactivities. A round of conversational exchange may also include one ormore recordings of additional conversation participant's conversationalactivities. A recording of one conversation participant's conversationalactivity may be correlated with a recording of another conversationparticipant's conversational activity. In effect, a round ofconversational exchange includes a unit of knowledge of how oneconversation participant acted relative to another conversationparticipant, and vice versa, in a part of a conversation. In oneexample, the first conversation participant may speak while the secondconversation participant observes (i.e. silent facial expressions,silent body movements, motionless silence, etc.) in a part of aconversation, therefore, a round of conversational exchange may includea recording of the first conversation participant's speakingconversational activity correlated with a recording of the secondconversation participant's silent facial expressions conversationalactivity. In another example, both conversation participants may observein a part of a conversation, therefore, a round of conversationalexchange may include a recording of the first conversation participant'ssilent body movements conversational activity correlated with arecording of the second conversation participant's silent facialexpressions conversational activity. In a further example, bothconversation participants may speak in a part of a conversation,therefore, a round of conversational exchange may include a recording ofthe first conversation participant's speaking conversational activitycorrelated with a recording of the second conversation participant'sspeaking conversational activity. A variety of arrangements ofrecordings of conversational activities and/or other data (i.e. Extrainfo 250, etc.) can be stored in rounds of conversational exchange. Insome aspects, the timing of recordings of conversational activities ofdifferent conversation participants may coincide, partially coincide, oroverlap. In further aspects, the number of recordings of conversationalactivities of one conversation participant may equal or differ from thenumber of recordings of conversational activities of anotherconversation participant. In general, a round of conversational exchangemay include any number, types, timing, and/or other properties ofrecordings of conversational activities of any number of conversationparticipants arranged in any conceivable combination. Generatingcomprises any action or operation by or for a Knowledge Structuring Unit110, and/or other disclosed elements.

At step 6140, the first round of conversational exchange is stored, thefirst round of conversational exchange being part of a stored pluralityof rounds of conversational exchange. Rounds of conversational exchangecan be stored in a memory unit or other repository. Once created orgenerated, rounds of conversational exchange can be used in/as neurons,nodes, vertices, or other elements in any of the data or knowledgestructures/arrangements (i.e. neural networks, graphs, sequences, etc.)used for facilitating learning functionalities herein. Rounds ofconversational exchange may be connected, interrelated, or interlinkedinto knowledge structures using statistical, artificial intelligence,machine learning, and/or other models or techniques. Such interconnectedor interrelated rounds of conversational exchange can be used forsimulating a conversation with a person (i.e. artificially intelligentconversation participant, etc.) in the person's absence, after theperson is deceased, or in any situation where a simulation of aconversation with the person may be needed. The interconnected rounds ofconversational exchange may be stored or organized into a knowledgebase(i.e. Knowledgebase 130, etc.). In some embodiments, knowledgebase maybe or include a neural network (i.e. Neural Network 130 a, etc.). Inother embodiments, knowledgebase may be or include a graph (i.e. Graph130 b, etc.). In further embodiments, knowledgebase may be or include acollection of sequences (i.e. Collection of Sequences 130 c, etc.). Infurther embodiments, knowledgebase may be or include a sequence (i.e.Sequence 133, etc.). In general, knowledgebase may be or include anydata structure, knowledge structure, or repository capable of storingthe knowledge of one or more conversations and/or other data. Storingcomprises any action or operation by or for a Knowledgebase 130, NeuralNetwork 130 a, Graph 130 b, Collection of Sequences 130 c, Sequence 133,Node 852, Layer 854, Connection 853, Substantial Similarity Comparison125, and/or other disclosed elements.

Referring to FIG. 18A, an embodiment of System for Using AIIMs 500implemented on User Device 80 is illustrated. System for Using AIIMs 500can also be implemented in any computing device or system instead of orin addition to User Device 80. In one example, System for Using AIIMs500 can be implemented in a dedicated device that can be owned bysomeone or given as a present to someone to simulate conversations withhis/her favorite person. In another example, System for Using AIIMs 500can be embedded into Host Device 98 such as a television device, aset-top box, a disc or other media player (i.e. DVD or Blue-ray player,etc.), a gaming device (i.e. Microsoft Xbox, Sony PlayStation, etc.), asmartphone (i.e. Apple iPhone, Samsung Galaxy, etc.), a mobile computer(i.e. tablet or laptop computer, etc.), a still or motion picturecamera, and/or others.

Referring to FIG. 18B, an embodiment of internal structure of System forUsing AIIMs 500 implemented on User Device 80 is illustrated. System forUsing AIIMs 500 comprises interconnected Decision-making Unit 510,Knowledgebase 130, and Activity Detector 160. Some embodiments ofActivity Detector 160 may include Picture Recognizer 163 and/orSpeech/Sound Recognizer 165. System for Using AIIMs 500 may be part ofUser Device 80. System for Using AIIMs 500 may utilize User Device's 80Picture-capturing Device 140, Sound-capturing Device 150, Display 21,and Sound-producing Device 30 to implement its functionalities. Otheradditional elements can be included as needed, or some of the disclosedones can be excluded, or a combination thereof can be utilized inalternate embodiments.

System for Using AIIMs 500 comprises any hardware, programs, or acombination thereof. System for Using AIIMs 500 comprises thefunctionality for simulating a conversation, System for Using AIIMs 500comprises the functionality for simulating a conversation based onknowledge of one or more conversations stored in Knowledgebase 130,Neural Network 130 a, Graph 130 b, Collection of Sequences 130 c,Sequence 133, and/or other data structure, knowledge structure, orrepository. System for Using AIIMs 500 comprises the functionality forsimulating verbal, visual, and/or other expressions or communicationthat resemble a person's conversational style or character, System forUsing AIIMs 500 comprises the functionality for simulating aconversation with one or more simulated persons. As such, System forUsing AIIMs 500 enables a simulation of a conversation among User 60 andone or more Artificially Intelligent Conversation Participants 55 (alsoreferred to as AI Conversation Participants 55, etc.). System for UsingAIIMs 500 can therefore be used to simulate a conversation with a personin the person's absence, after the person is deceased, or in anysituation where a simulation of a conversation with the person may beneeded. For example, System for Using AIIMs 500 can be used to simulatea conversation with a parent, predecessor (i.e. grandparent, etc.),spouse, friend, historical figure, famous person (i.e. scientist,athlete, actor, musician, politician, etc.), and/or other persons. Aperson can even talk to an artificially intelligent interactive versionof him/herself. System for Using AIIMs 500 also comprises otherfunctionalities disclosed herein.

In one example, the teaching presented by the disclosure can beimplemented in a device or system for using AIIMs. The device or systemmay include one or more processor circuits. The device or system mayfurther include a memory unit, coupled to the one or more processorcircuits, that stores a plurality of rounds of conversational exchangeincluding a first round of conversational exchange, the first round ofconversational exchange comprising a recording of a first conversationparticipant's first conversational activity and a recording of a secondconversation participant's first conversational activity. The device orsystem may further include a picture-capturing device, coupled to theone or more processor circuits, configured to capture a stream ofdigital pictures of a user. The device or system may further include asound-capturing device, coupled to the one or more processor circuits,configured to capture a stream of digital sound samples of the user. Theone or more processor circuits may be configured to detect the user'sfirst conversational activity from at least one of the stream of digitalpictures of the user or the stream of digital sound samples of the user.The one or more processor circuits may also be configured to compare atleast one portion of a recording of the user's first conversationalactivity with at least one portion of the recording of the firstconversation participant's first conversational activity. The one ormore processor circuits may also be configured to determine that asimilarity between at least one portion of the recording of the user'sfirst conversational activity and at least one portion of the recordingof the first conversation participant's first conversational activityexceeds a similarity threshold. The one or more processor circuits mayalso be configured to cause a display and a sound-producing device toplay at least one portion of the recording of the second conversationparticipant's first conversational activity. Any of the operations ofthe described elements can be performed repeatedly and/or in differentorders in alternate embodiments. In some aspects, the one or moreprocessor circuits, the memory unit, the picture-capturing device, thesound-capturing device, the display, and the sound-producing device arepart of a single device. In other aspects, at least one of: the one ormore processor circuits or the memory unit are part of a server,whereas, the picture-capturing device, the sound-capturing device, thedisplay, and the sound-producing device are part of a user device, theuser device coupled to the server via a network. Other additionalelements can be included as needed, or some of the disclosed ones can beexcluded, or a combination thereof can be utilized in alternateembodiments. The device or system for using AIIMs can also include anyactions or operations of any of the disclosed methods such as methods6100 and/or 6200 (later described).

User 60 (also referred to simply as user, etc.) can be a human user. Inone example, User 60 can be Conversation Participant 50 a whoseconversations with Conversation Participant 50 b have been learned bySystem for Learning AIIMs 100 and who wishes to simulate a conversationwith Conversation Participant 50 b via System for Using AIIMs 500. Inanother example, User 60 can be any person who wishes to simulate aconversation via System for Using AIIMs 500. In some aspects, User 60can be a non-human User 60. The non-human User 60 may include anydevice, apparatus, or system comprising conversational functionalitiessuch as a humanoid or other robot, user simulated on a computing device,and/or others.

Display 21 comprises the functionality for displaying visualinformation, and/or other functionalities. Examples of a typical Display21 include a liquid crystal display (LCD), cathode ray tube (CRT)display, and/or other display. In some aspects, Display 21 may include aprojector, a hologram producing device, and/or other device fordisplaying visual information. In further aspects, Display 21 may beplaced on the front of a robot's head to simulate a face. In furtheraspects, instead using Display 21, the learned verbal and/or visualinformation can be transformed into physical movements of a robot's face(i.e. mouth, eyes, etc.) and/or other robot's body parts, therebysimulating a conversation with a physical artificially intelligentconversation participant. Such movements can be implemented by engagingmotors or actuators in the robot's face or other robot's body parts.

Sound-producing Device 30 comprises the functionality for producingsound, and/or other functionalities. Examples of Sound-producing Device30 include a built-in or an external speaker, headphone, and/or othersound producing device.

Decision-making Unit 510 comprises the functionality for determiningactivities (i.e. Conversational Activities 210, etc.) of AI ConversationParticipant 55, and/or other functionalities. Decision-making Unit 510comprises functions, rules, and/or logic to determine or anticipatewhich Conversational Activity 210 is most suitable or likely to be usedor implemented by AI Conversation Participant 55 in a simulatedconversation. Similarly, Decision-making Unit 510 comprises thefunctionality to determine which Conversational Activity 210 is secondmost suitable or likely to be used or implemented, which ConversationalActivity 210 is third most suitable or likely to be used or implemented,and so on. Furthermore, Decision-making Unit 510 comprises thefunctionality to determine a sequence or order in which ConversationalActivities 210 are most suitable or likely to be used or implemented byAI Conversation Participant 55 in a simulated conversation. In someaspects, Decision-making Unit 510 may determine ConversationalActivities 210 of AI Conversation Participant 55 by performingSubstantial Similarity Comparisons 125 of Conversational Activities 210from Activity Detector 160 with Conversational Activities 210 fromRounds of Conversational Exchange 200 stored in Knowledgebase 130,Neural Network 130 a, Graph 130 b, Collection of Sequences 130 c,Sequence 133, and/or other data structure, knowledge structure, orrepository, A Round of Conversational Exchange 200 includes a unit ofknowledge (i.e. correlated Conversational Activities 210, etc.) of howone Conversation Participant 50 acted relative to another ConversationParticipant 50, and vice versa, in a part of a conversation aspreviously described. When Conversational Activities 210 with similarcontent, structure, and/or other properties are detected involving User60 in the future, the learned Conversational Activities 210 of one ormore Conversation Participants 50 stored in Rounds of ConversationalExchange 200 can be determined or anticipated by Decision-making Unit510, thereby simulating a conversation with one or more AI ConversationParticipants 55.

In some embodiments, Decision-making Unit 510 can perform SubstantialSimilarity Comparisons 125 of User's 60 Conversational Activity 210 orportion thereof from Activity Detector 160 with ConversationalActivities 210 or portions thereof from Rounds of ConversationalExchange 200 in Knowledgebase 130, Neural Network 130 a, Graph 130 b,Collection of Sequences 130 c, Sequence 133, and/or other datastructure, knowledge structure, or repository (also referred to asKnowledgebase 130, etc.). In some implementations where Rounds ofConversational Exchange 200 similar to the one shown in FIG. 6A areused, if a substantially or otherwise similar Conversational Activity210 or portion thereof is found in a Round of Conversational Exchange200 from Knowledgebase 130, a concurrent Conversational Activity 210 orportion thereof of AI Conversation Participant 55 can be anticipated ina correlated Conversational Activity 210 or portion thereof from theRound of Conversational Exchange 200, Also, subsequent ConversationalActivity 210 or portion thereof of AI Conversation Participant 55 can beanticipated in a Conversational Activity 210 or portion thereof fromRound of Conversational Exchange 200 connected with the prior Round ofConversational Exchange 200. In some implementations where Rounds ofConversational Exchange 200 similar to the one shown in FIG. 6C areused, if a substantially or otherwise similar Conversational Activity210 or portion thereof is found in a Round of Conversational Exchange200 from Knowledgebase 130, a concurrent Conversational Activity 210 orportion thereof of AI Conversation Participant 55 can be anticipated ina correlated Conversational Activity 210 or portion thereof from theRound of Conversational Exchange 200. Also, subsequent ConversationalActivity 210 or portion thereof of AI Conversation Participant 55 can beanticipated in a subsequent Conversational Activity 210 or portionthereof from the Round of Conversational Exchange 200.

Decision-making Unit 510 can utilize various elements and/or techniquesfor selecting a path of Rounds of Conversational Exchange 200 (orConversational Activities 210 therein) through Neural Network 130 a, forexample, Although, these elements and/or techniques are described usingNeural Network 130 a below, they can similarly be used in anyKnowledgebase 130, Graph 130 b, Collection of Sequences 130 c, Sequence133, and/or other data structure or repository.

In some embodiments, Decision-making Unit 510 can utilize similarityindex in selecting Rounds of Conversational Exchange 200 (orConversational Activities 210 therein) in a path through Neural Network130 a. Similarity index may indicate how well a Conversational Activity210 or portion thereof is matched with another Conversational Activity210 or portion thereof as previously described. Substantial SimilarityComparison 125 can be used to generate a similarity index whethersubstantial or other similarity of the compared ConversationalActivities 210 or portions thereof is achieved or not as previouslydescribed. In one example, Decision-making Unit 510 may select a Roundof Conversational Exchange 200 comprising Conversational Activity 210with the highest similarity index even if Connection 853 pointing tothat Round of Conversational Exchange 200 has less than the highestweight. Therefore, similarity index or other such element or parametercan override or disregard the weight of a Connection 853 or otherelement. In another example, Decision-making Unit 510 may select a Roundof Conversational Exchange 200 comprising Conversational Activity 210whose similarity index is higher than or equal to a weight of Connection853 pointing to that Round of Conversational Exchange 200. In a furtherexample, Decision-making Unit 510 may select a Round of ConversationalExchange 200 comprising Conversational Activity 210 whose similarityindex is lower than or equal to a weight of Connection 853 pointing tothat Round of Conversational Exchange 200. Therefore, similarity indexcan be set to be more, less, or equally important than a weight of aConnection 853, In some aspects, a minimum similarity index or thresholdcan be set for a Conversational Activity 210. In other aspects,different minimum similarity indexes or thresholds can be set fordifferent Conversational Activities 210. Minimum similarity indexes orthresholds can also be set for any other elements such as Rounds ofConversational Exchange 200, Layers 854, and/or other elements. Forexample, a higher minimum similarity index or threshold can be set forlower numbered Layers 854 and decreased for the remaining Layers 854.Any other settings of a minimum similarity index can be utilized inalternate embodiments.

In other embodiments, Decision-making Unit 510 can utilize Connections853 in selecting Rounds of Conversational Exchange 200 (orConversational Activities 210 therein) in a path through Neural Network130 a. Decision-making Unit 510 can take into account weights ofConnections 853 among the interconnected Rounds of ConversationalExchange 200 in choosing from which Round of Conversational Exchange 200to compare a Conversational Activity 210 first, second, third, and soon. Specifically, for instance, Decision-making Unit 510 can performSubstantial Similarity Comparison 125 with Conversational Activity 210from Round of Conversational Exchange 200 pointed to by the highestweight Connection 853 first, Conversational Activity 210 from Round ofConversational Exchange 200 pointed to by the second highest weightConnection 853 second, and so on. In some aspects, Decision-making Unit510 can stop performing Substantial Similarity Comparisons 125 in aparticular Layer 854 as soon as it finds a substantially similarConversational Activity 210 from an interconnected Round ofConversational Exchange 200. In other aspects, Decision-making Unit 510may only follow the highest weight Connection 853 to arrive at a Roundof Conversational Exchange 200 comprising Conversational Activity 210 tobe compared, thereby disregarding Connections 853 with less than thehighest weight.

In further embodiments, Decision-making Unit 510 can utilize a bias toadjust similarity index, weight of a Connection 853, and/or otherelement or parameter used in selecting Rounds of Conversational Exchange200 (or Conversational Activities 210 therein) in a path through NeuralNetwork 130 a. In one example, Decision-making Unit 510 may select aRound of Conversational Exchange 200 comprising Conversational Activity210 whose similarity index multiplied by or adjusted for a bias ishigher than or equal to a weight of Connection 853 pointing to thatRound of Conversational Exchange 200. In another example,Decision-making Unit 510 may select a Round of Conversational Exchange200 comprising Conversational Activity 210 whose similarity indexmultiplied by or adjusted for a bias is lower than or equal to a weightof Connection 853 pointing to that Round of Conversational Exchange 200.In a further example, bias can be used to resolve deadlock situationswhere similarity index is equal to a weight of a Connection 853. In someaspects, bias can be expressed in percentages such as 0.3 percent, 1.2percent, 25.7 percent, 79.8 percent, 99.9 percent, 100.1 percent, 155.4percent, 298.6 percent, 1105.5 percent, and so on. For example, a biasbelow 100 percent decreases an element or parameter to which it isapplied, a bias equal to 100 percent does not change the element orparameter to which it is applied, and a bias higher than 100 percentincreases the element or parameter to which it is applied. In general,any amount of bias can be utilized. Bias can be applied to one or moreof a weight of a Connection 853, similarity index, any other element orparameter, and/or all or any combination of them. Also, different biasescan be applied to each of a weight of a Connection 853, similarityindex, or any other element or parameter. For example, 30 percent biascan be applied to similarity index and 15 percent bias can be applied toa weight of a Connection 853. Also, different biases can be applied tovarious Layers 854 of Neural Network 130 a, and/or other disclosedelements. Bias can be defined by a user, by AIIM system administrator,or automatically by the system based on experience, testing, inquiry,analysis, synthesis, or other techniques, knowledge, or input.

Any other element and/or technique can be utilized in selecting Roundsof Conversational Exchange 200 (or Conversational Activities 210therein) in a path through Neural Network 130 a.

Referring to FIG. 19, an embodiment of internal structure of System forUsing AIIMs 500 implemented as a network service is illustrated. Systemfor Using AIIMs 500 comprises interconnected Decision-making Unit 510,Knowledgebase 130, and Activity Detector 160. Some embodiments ofActivity Detector 160 may include Picture Recognizer 163 and/orSpeech/Sound Recognizer 165. System for Using AIIMs 500 or any elementthereof may reside or operate on Server 90, which is accessible by UserDevice 80 over Network 95. User Device 80 comprises Picture-capturingDevice 140, Sound-capturing Device 150, Display 21, and Sound-producingDevice 30. Other additional elements can be included as needed, or someof the disclosed ones can be excluded, or a combination thereof can beutilized in alternate embodiments.

In some embodiments, System for Using AIIMs 500 operating on Server 90can use knowledge (i.e. Knowledgebase 130, etc.) of conversations amongConversation Participants 50 learned by System for Learning AIIMs 100that itself may be operating on Server 90 as previously described.Conversation Participant 50 or any other user can utilize System forUsing AIIMs 500 operating on Server 90 to simulate a conversation withAI Conversation Participant 55 on his/her User Device 80 via Network 95.As such, System for Using AIIMs 500 implemented as a network service maybe available to members of the network service (i.e. membership orsubscription based network service, etc.) or to all the world's Users 60(i.e. freely available network service, etc.) who wish to simulateconversations.

Referring to FIG. 20, an exemplary embodiment of selecting a path ofRounds of Conversational Exchange 200 (or Conversational Activities 210therein) through Neural Network 130 a is illustrated. Neural Network 130a may include knowledge (i.e. interconnected Rounds of ConversationalExchange 200, etc.) of one or more conversations between ConversationParticipants 50 a and 50 b. In this example, Round of ConversationalExchange 200 comprises a Conversational Activity 210 of ConversationParticipant 50 a correlated with a Conversational Activity 210 ofConversation Participant 50 b similar to the one shown in FIG. 6A. User60 may be the same person as Conversation Participant 50 a or any otherperson. The conversation is simulated with AI Conversation Participant55 who uses knowledge of Conversation Participant 50 b stored in NeuralNetwork 130 a to resemble Conversation Participant 50 b. SubstantialSimilarity Comparison 125 can be used to determine substantialsimilarity of the compared Conversational Activities 210 or portionsthereof. Such substantial similarity, if achieved, may be used primarilyfor selecting a path through Neural Network 130 a, whereas, weight ofany Connection 853 and/or other elements may be used secondarily, forexample. Optional ancillary Substantial Similarity Comparisons 125 canbe selectively performed where applicable (i.e. with speakingConversational Activities 210 or portions thereof, etc.) to enhancedecision making (i.e. narrow down choices, etc.) as later described. Asthe simulated conversation progresses, Decision-making Unit 510 canreceive User's 60 Conversational Activities 210 or portions thereof fromActivity Detector 160.

For example, Decision-making Unit 510 can perform Substantial SimilarityComparisons 125 of User's 60 Conversational Activity 210 ia (i.e.speaking, etc.) or portion thereof from Activity Detector 160 withConversation Participant's 50 a Conversational Activities 210 orportions thereof from one or more Rounds of Conversational Exchange 200in Layer 854 a (or any other one or more Layers 854, etc.). ConversationParticipant's 50 a Conversational Activity 210 or portion thereof fromRound of Conversational Exchange 200 ia may be found substantiallysimilar with the highest similarity. Decision-making Unit 510 can playSub-stream of Digital Pictures 145 and Sub-stream of Digital SoundSamples 155 of Conversation Participant's 50 b Conversational Activity210 from Round of Conversational Exchange 200 ia, thereby simulating AIConversation Participant's 55 activity (i.e. motionless silence, etc.)during User's 60 Conversational Activity 210 ia (i.e. speaking, etc.).Playing Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of Conversation Participant's 50 b ConversationalActivity 210 from Round of Conversational Exchange 200 ia can start atany time during Substantial Similarity Comparisons 125 such as when aninitial similarity is reached as later described. Decision-making Unit510 can then perform Substantial Similarity Comparisons 125 of User's 60Conversational Activity 210 ib (i.e. silent facial expressions, etc.) orportion thereof from Activity Detector 160 with ConversationParticipant's 50 a Conversational Activities 210 or portions thereoffrom one or more Rounds of Conversational Exchange 200 in correspondingLayer 854 b interconnected with Round of Conversational Exchange 200 ia.Conversation Participant's 50 a Conversational Activities 210 orportions thereof from multiple Rounds of Conversational Exchange 200 maybe found substantially similar. To enhance decision making (i.e. narrowdown choices, etc.), Decision-making Unit 510 can also perform ancillarySubstantial Similarity Comparisons 125 of User's 60 ConversationalActivity 210 ia (i.e. speaking, etc.) or portion thereof from ActivityDetector 160 with Conversation Participant's 50 b ConversationalActivities 210 (i.e. speaking, etc.) or portions thereof from the Roundsof Conversational Exchange 200 comprising substantially similarConversation Participant's 50 a Conversational Activities 210 orportions thereof. Conversation Participant's 50 b ConversationalActivity 210 or portion thereof from Round of Conversational Exchange200 ib may be found at least partially similar. Decision-making Unit 510may follow Connection 853 h, and play Sub-stream of Digital Pictures 145and Sub-stream of Digital Sound Samples 155 of ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 200 ib, thereby simulating AI ConversationParticipant's 55 activity (i.e. speaking, etc.) during User's 60Conversational Activity 210 ib (i.e. silent facial expressions, etc.).Playing Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of Conversation Participant's 50 b ConversationalActivity 210 from Round of Conversational Exchange 200 ib can start atany time during Substantial Similarity Comparisons 125 such as when aninitial similarity is reached as later described. Decision-making Unit510 can then perform Substantial Similarity Comparisons 125 of User's 60Conversational Activity 210 ic (i.e. speaking, etc.) or portion thereoffrom Activity Detector 160 with Conversation Participant's 50 aConversational Activities 210 or portions thereof from one or moreRounds of Conversational Exchange 200 in corresponding Layer 854 cinterconnected with Round of Conversational Exchange 200 ib.Conversation Participant's 50 a Conversational Activity 210 or portionthereof from Round of Conversational Exchange 2001 c may be foundsubstantially similar. Decision-making Unit 510 may follow Connection853 i disregarding its less than highest weight, and play Sub-stream ofDigital Pictures 145 and Sub-stream of Digital Sound Samples 155 ofConversation Participant's 50 b Conversational Activity 210 from Roundof Conversational Exchange 200 ic, thereby simulating AI ConversationParticipant's 55 activity (i.e. motionless silence, etc.) during User's60 Conversational Activity 210 ic (i.e. speaking, etc.). PlayingSub-stream of Digital Pictures 145 and Sub-stream of Digital SoundSamples 155 of Conversation Participant's 50 b Conversational Activity210 from Round of Conversational Exchange 200 ic can start at any timeduring Substantial Similarity Comparisons 125 such as when an initialsimilarity is reached as later described. Decision-making Unit 510 canthen perform Substantial Similarity Comparisons 125 of User's 60Conversational Activity 210 id (i.e. silent body movements, etc.) orportion thereof from Activity Detector 160 with ConversationParticipant's 50 a Conversational Activities 210 or portions thereoffrom one or more Rounds of Conversational Exchange 200 in correspondingLayer 854 d interconnected with Round of Conversational Exchange 200 ic.None of Conversation Participant's 50 a Conversational Activities 210 orportions thereof from one or more Rounds of Conversational Exchange 200in Layer 854 d interconnected with Round of Conversational Exchange 200ic may be found substantially similar. Decision-making Unit 510 mayfollow the highest weight Connection 853 j, and play Sub-stream ofDigital Pictures 145 and Sub-stream of Digital Sound Samples 155 ofConversation Participant's 50 b Conversational Activity 210 from Roundof Conversational Exchange 200 id, thereby simulating AI ConversationParticipant's 55 activity (i.e. speaking, etc.) during User's 60Conversational Activity 2101 d (i.e. silent body movements, etc.).Playing Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of Conversation Participant's 50 b ConversationalActivity 210 from Round of Conversational Exchange 200 id can start atany time during Substantial Similarity Comparisons 125 such as when adetermination is made that an initial similarity has not been reached aslater described. Since Connection 853 k is the only connection fromRound of Conversational Exchange 200 id, Decision-making Unit 510 mayfollow Connection 853 k, and play Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 200ie, thereby simulating AI Conversation Participant's 55 activity (i.e.silent body movements, etc.) during User's 60 Conversational Activity2101 e (i.e. speaking, etc.). Decision-making Unit 510 can implementsimilar logic or process for any additional Conversational Activities210 from Activity Detector 160, and so on.

In some aspects, Decision-making Unit 510 may perform the aforementionedancillary Substantial Similarity Comparisons 125 to narrow down thechoice of Rounds of Conversational Exchange 200 comprisingConversational Activities 210 found to be substantially or otherwisesimilar by the main Substantial Similarity Comparisons 125, therebyenabling a more accurate decision making. For example, Decision-makingUnit 510 can perform ancillary Substantial Similarity Comparisons 125 ofUser's 60 prior Conversational Activities 210 (i.e. speaking, etc.) orportions thereof from Activity Detector 160 with ConversationalActivities 210 (i.e. speaking, etc.) or portions thereof from one ormore Rounds of Conversational Exchange 200 currently being processed asdescribed in the example above. Ancillary Substantial SimilarityComparisons 125 may be especially useful in comparing speakingConversational Activities 210 as Conversation Participants 50 may repeatsome of each other's words in subsequent speaking ConversationalActivities 210. In other aspects, Decision-making Unit 510 may performancillary Substantial Similarity Comparisons 125 to look forward andidentify subsequent similar Conversational Activities 210 even beforeUser's 60 current Conversational Activity 210 is fully received. Forexample, while performing main Substantial Similarity Comparisons 125 ofConversational Activities 210 in a current Layer 854, Decision-makingUnit 510 may perform ancillary Substantial Similarity Comparisons 125 ofConversational Activities 210 or portions thereof from Rounds ofConversational Exchange 200 in a subsequent Layer 854 interconnectedwith Round of Conversational Exchange 200 currently being processed.Ancillary Substantial Similarity Comparisons 125 can be performedconcurrently with main Substantial Similarity Comparisons 125.Concurrent and/or forward-looking ancillary Substantial SimilarityComparisons 125 may enhance real time performance of AI ConversationParticipant 55 in a simulated conversation. Ancillary SubstantialSimilarity Comparisons 125 can also be omitted.

The foregoing exemplary embodiment provides an example of utilizing acombination of Substantial Similarity Comparisons 125, ancillarycomparisons, weights of Connections 853, and/or other elements and/ortechniques. It should be understood that any of these elements and/ortechniques can be omitted, used in a different combination, or used incombination with other elements and/or techniques, in which case thepath of Rounds of Conversational Exchange 200 (or ConversationalActivities 210 therein) through Neural Network 130 a would be affectedaccordingly. In one example, Substantial Similarity Comparisons 125 canbe omitted, in which case weights of Connections 853 can be primarilyutilized for deciding which Rounds of Conversational Exchange 200 (orConversational Activities 210 therein) to select in a path throughNeural Network 130 a, in another example, weights of Connections 853 canbe omitted, in which case Substantial Similarity Comparisons 125 can beprimarily utilized for deciding which Rounds of Conversational Exchange200 (or Conversational Activities 210 therein) to select in a paththrough Neural Network 130 a. These elements and/or techniques cansimilarly be utilized in Graph 130 b, Collections of Sequences 130 c,Sequence 133, and/or other data structures or repositories. Any of thepreviously described arrangements of Conversational Activities 210 in aRound of Conversational Exchange 200, and/or other elements can beutilized as well, One of ordinary skill in art will understand that thisexemplary embodiment is described merely as an example of a variety ofpossible implementations, and that while all of its variations are toovoluminous to describe, they are within the scope of this disclosure.

Referring to FIG. 21, an exemplary embodiment of selecting a path ofRounds of Conversational Exchange 200 (or Conversational Activities 210therein) through Neural Network 130 a is illustrated. Neural Network 130a may include knowledge (i.e. interconnected Rounds of ConversationalExchange 200, etc.) of one or more conversations between ConversationParticipants 50 a and 50 b. In this example, Round of ConversationalExchange 200 comprises a Conversational Activity 210 of ConversationParticipant 50 a correlated with Conversational Activity 210 ofConversation Participant 50 b and a subsequent Conversational Activity210 of Conversation Participant 50 a correlated with a subsequentConversational Activity 210 of Conversation Participant 50 b similar tothe one shown in FIG. 6C. User 60 may be the same person as ConversationParticipant 50 a or any other person. The conversation is simulated withAI Conversation Participant 55 who uses knowledge of ConversationParticipant 50 b stored in Neural Network 130 a to resemble ConversationParticipant 50 b. Substantial Similarity Comparison 125 can be used todetermine substantial similarity of the compared ConversationalActivities 210 or portions thereof. Such substantial similarity, ifachieved, may be used primarily for selecting a path through NeuralNetwork 130 a, whereas, weight of any Connection 853 and/or otherelements may be used secondarily, for example. In this example, as thesimulated conversation progresses, Decision-making Unit 510 can be setupto receive User's 60 speaking Conversational Activities 210 or portionsthereof from Activity Detector 160 while other Conversational Activities210 from Activity Detector 160 may be omitted.

For example, Decision-making Unit 510 can perform Substantial SimilarityComparisons 125 of User's 60 Conversational Activity 210 ja (i.e.speaking, etc.) or portion thereof from Activity Detector 160 withConversation Participant's 50 a Conversational Activities 210 (i.e.speaking, etc.) or portions thereof from one or more Rounds ofConversational Exchange 200 in Layer 854 a (or any other one or moreLayers 854, etc.). Conversation Participant's 50 a ConversationalActivity 210 or portion thereof from Round of Conversational Exchange200 ja may be found substantially similar with highest similarity.Decision-making Unit 510 may play Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of a correlated ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 200 ja, thereby simulating AI ConversationParticipant's 55 activity (i.e. silent facial expressions, etc.) duringUser's 60 Conversational Activity 210 ja (i.e. speaking, etc.). PlayingSub-stream of Digital Pictures 145 and Sub-stream of Digital SoundSamples 155 of the correlated Conversation Participant's 50 bConversational Activity 210 from Round of Conversational Exchange 200 jacan start at any time during Substantial Similarity Comparisons 125 suchas when an initial similarity is reached as later described.Decision-making Unit 510 may also play Sub-stream of Digital Pictures145 and Sub-stream of Digital Sound Samples 155 of a subsequentConversation Participant's 50 b Conversational Activity 210 from Roundof Conversational Exchange 200 ja, thereby simulating AI ConversationParticipant's 55 activity (i.e. speaking, etc.) subsequent to User's 60Conversational Activity 210 ja (i.e. speaking, etc.). Decision-makingUnit 510 can then perform Substantial Similarity Comparisons 125 ofUser's 60 Conversational Activity 210 jb (i.e. speaking, etc.) orportion thereof from Activity Detector 160 with ConversationParticipant's 50 a Conversational Activities 210 (i.e. speaking, etc.)or portions thereof from one or more Rounds of Conversational Exchange200 in corresponding Layer 854 b interconnected with Round ofConversational Exchange 200 ja. Conversation Participant's 50 aConversational Activity 210 or portion thereof from Round ofConversational Exchange 200 jb may be found substantially similar withhighest similarity, Decision-making Unit 510 may follow Connection 853 mdisregarding its less than highest weight, and play Sub-stream ofDigital Pictures 145 and Sub-stream of Digital Sound Samples 155 of acorrelated Conversation Participant's 50 b Conversational Activity 210from Round of Conversational Exchange 200 jb, thereby simulating AIConversation Participant's 55 activity (i.e. silent body movements,etc.) during User's 60 Conversational Activity 210 jb (i.e. speaking,etc.). Playing Sub-stream of Digital Pictures 145 and Sub-stream ofDigital Sound Samples 155 of the correlated Conversation Participant's50 b Conversational Activity 210 from Round of Conversational Exchange200 jb can start at any time during Substantial Similarity Comparisons125 such as when an initial similarity is reached as later described.Decision-making Unit 510 may also play Sub-stream of Digital Pictures145 and Sub-stream of Digital Sound Samples 155 of a subsequentConversation Participant's 50 b Conversational Activity 210 from Roundof Conversational Exchange 200 jb, thereby simulating AI ConversationParticipant's 55 activity (i.e. speaking, etc.) subsequent to User's 60Conversational Activity 210 jb (i.e. speaking, etc.). Since Connection853 n is the only connection from Round of Conversational Exchange 200jb, Decision-making Unit 510 may follow Connection 853 n, and playSub-stream of Digital Pictures 145 and Sub-stream of Digital SoundSamples 155 of a correlated Conversation Participant's 50 bConversational Activity 210 from Round of Conversational Exchange 200jc, thereby simulating AI Conversation Participant's 55 activity (i.e.motionless silence, etc.) during User's 60 Conversational Activity 210jc (i.e. speaking, etc.). Decision-making Unit 510 may also playSub-stream of Digital Pictures 145 and Sub-stream of Digital SoundSamples 155 of a subsequent Conversation Participant's 50 bConversational Activity 210 from Round of Conversational Exchange 200jc, thereby simulating AI Conversation Participant's 55 activity (i.e.speaking, etc.) subsequent to User's 60 Conversational Activity 210 jc(i.e. speaking, etc.). Decision-making Unit 510 can then performSubstantial Similarity Comparisons 125 of User's 60 ConversationalActivity 210 jd (i.e. speaking, etc.) or portion thereof from ActivityDetector 160 with Conversation Participant's 50 a ConversationalActivities 210 (i.e. speaking, etc.) or portions thereof from one ormore Rounds of Conversational Exchange 200 in corresponding Layer 854 dinterconnected with Round of Conversational Exchange 200 jc, None ofConversation Participant's 50 a Conversational Activities 210 (i.e.speaking, etc.) or portions thereof from Rounds of ConversationalExchange 200 in Layer 854 d interconnected with Round of ConversationalExchange 200 jc may be found substantially similar. Decision-making Unit510 may follow the highest weight Connection 853 o, and play Sub-streamof Digital Pictures 145 and Sub-stream of Digital Sound Samples 155 of acorrelated Conversation Participant's 50 b Conversational Activity 210from Round of Conversational Exchange 200 jd, thereby simulating AIConversation Participant's 55 activity (i.e. silent facial expressions,etc.) during User's 60 Conversational Activity 210 jd (i.e. speaking,etc.). Playing Sub-stream of Digital Pictures 145 and Sub-stream ofDigital Sound Samples 155 of the correlated Conversation Participant's50 b Conversational Activity 210 from Round of Conversational Exchange200 jd can start at any time during Substantial Similarity Comparisons125 such as when a determination is made that an initial similarity hasnot been reached as later described. Decision-making Unit 510 may alsoplay Sub-stream of Digital Pictures 145 and Sub-stream of Digital SoundSamples 155 of a subsequent Conversation Participant's 50 bConversational Activity 210 from Round of Conversational Exchange 200jd, thereby simulating AI Conversation Participant's 55 activity (i.e.speaking, etc.) subsequent to User's 60 Conversational Activity 210 jd(i.e. speaking, etc.). Decision-making Unit 510 can then performSubstantial Similarity Comparisons 125 of User's 60 ConversationalActivity 210 je (i.e. speaking, etc.) or portion thereof from ActivityDetector 160 with Conversation Participant's 50 a ConversationalActivities 210 (i.e. speaking, etc.) or portions thereof from one ormore Rounds of Conversational Exchange 200 in corresponding Layer 854 einterconnected with Round of Conversational Exchange 200 jd.Conversation Participant's 50 a Conversational Activity 210 or portionthereof from Round of Conversational Exchange 200 je may be foundsubstantially similar with highest similarity. Decision-making Unit 510may follow Connection 853 p, and play Sub-stream of Digital Pictures 145and Sub-stream of Digital Sound Samples 155 of a correlated ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 200 je, thereby simulating AI ConversationParticipant's 55 activity (i.e. silent body movements, etc.) duringUser's 60 Conversational Activity 210 je (i.e. speaking, etc.). PlayingSub-stream of Digital Pictures 145 and Sub-stream of Digital SoundSamples 155 of the correlated Conversation Participant's 50 bConversational Activity 210 from Round of Conversational Exchange 200 jecan start at any time during Substantial Similarity Comparisons 125 suchas when an initial similarity is reached as later described.Decision-making Unit 510 may also play Sub-stream of Digital Pictures145 and Sub-stream of Digital Sound Samples 155 of a subsequentConversation Participant's 50 b Conversational Activity 210 from Roundof Conversational Exchange 200 je, thereby simulating AI ConversationParticipant's 55 activity (i.e. speaking, etc.) subsequent to User's 60Conversational Activity 210 je (i.e. speaking, etc.). Decision-makingUnit 510 can implement similar logic or process for any additionalConversational Activities 210 from Activity Detector 160, and so on.

In both of the above described and/or other exemplary embodiments, anytime that substantial similarity or other similarity threshold is notachieved in any of the compared Conversational Activities 210 orportions thereof, instead of following the highest weight Connection 853or the only Connection 853, Decision-making Unit 510 can decide to lookfor a substantially or otherNise similar Conversational Activity 210 orportion thereof elsewhere in Neural Network 130 a such as in any Layer854 subsequent to a current Layer 854, in the first Layer 854, in theentire Neural Network 130 a, and/or others.

In both of the above described and/or other exemplary embodiments, asthe simulated conversation progresses, a history (i.e. sequence, etc.)of User's 60 Conversational Activities 210 or portions thereof becomesavailable, which can then be collectively compared with ConversationalActivities 210 or portions thereof from Rounds of ConversationalExchange 200 in paths of Neural Network 130 a. Collectively comparingConversational Activities 210 or portions thereof may enableDecision-making Unit 510 to more accurately determine or anticipate AIConversation Participant's 55 activities in the simulated conversation.For example, Decision-making Unit 510 can perform collective SubstantialSimilarity Comparisons 125 of a history of User's 60 ConversationalActivities 210 or portions thereof from Activity Detector 160 withConversational Activities 210 or portions thereof from Rounds ofConversational Exchange 200 in one or more paths of Neural Network 130a. As additional User's 60 Conversational Activities 210 or portionsthereof from Activity Detector 160 become available, Decision-makingUnit 510 can use a longer history of User's 60 Conversational Activities210 or portions thereof to compare with corresponding ConversationalActivities 210 or portions thereof from Rounds of ConversationalExchange 200 in paths of Neural Network 130 a. In each cycle ofcomparisons, Decision-making Unit 510 may choose the most similar of thecompared paths and switch to a more suitable path based on suchcollective similarity determinations.

The foregoing exemplary embodiment provides an example of utilizing acombination of Substantial Similarity Comparisons 125, weights ofConnections 853, and/or other elements and/or techniques. It should beunderstood that any of these elements and/or techniques can be omitted,used in a different combination, or used in combination with otherelements and/or techniques, in which case the path of Rounds ofConversational Exchange 200 (or Conversational Activities 210 therein)through Neural Network 130 a would be affected accordingly. Also, any ofthe elements and/or techniques utilized in other examples or embodimentsdescribed herein such as ancillary comparisons, concurrent comparisons,various arrangements of Conversational Activities 210 in a Round ofConversational Exchange 200, and/or others can similarly be utilized inthis exemplary embodiment, One of ordinary skill in art will understandthat this exemplary embodiment is described merely as an example of avariety of possible implementations, and that while all of itsvariations are too voluminous to describe, they are within the scope ofthis disclosure.

Referring to FIG. 22, an exemplary embodiment of selecting a path ofRounds of Conversational Exchange 200 (or Conversational Activities 210therein) through Graph 130 b is illustrated. Graph 130 b may includeknowledge (i.e. interconnected Rounds of Conversational Exchange 200,etc.) of one or more conversations between Conversation Participants 50a and 50 b. In this example, Round of Conversational Exchange 200comprises a Conversational Activity 210 of Conversation Participant 50 acorrelated with a Conversational Activity 210 of ConversationParticipant 50 b similar to the one shown in FIG. 6A. User 60 may be thesame person as Conversation Participant 50 a or any other person. Theconversation is simulated with AI Conversation Participant 55 who usesknowledge of Conversation Participant 50 b stored in Graph 130 b toresemble Conversation Participant 50 b. Substantial SimilarityComparison 125 can be used to determine substantial similarity of thecompared Conversational Activities 210 or portions thereof. Suchsubstantial similarity, if achieved, may be used primarily for selectinga path through Graph 130 b, whereas, weight of any Connection 853 and/orother elements may be used secondarily, for example. Optional ancillarySubstantial Similarity Comparisons 125 can be selectively performedwhere applicable (i.e. with speaking Conversational Activities 210 orportions thereof; etc.) to enhance decision making (i.e. narrow downchoices, etc.) as later described. As the simulated conversationprogresses, Decision-making Unit 510 can receive User's 60Conversational Activities 210 or portions thereof from Activity Detector160.

For example, Decision-making Unit 510 can perform Substantial SimilarityComparisons 125 of User's 60 Conversational Activity 210 ia (i.e.speaking, etc.) or portion thereof from Activity Detector 160 withConversation Participant's 50 a Conversational Activities 210 orportions thereof from one or more Rounds of Conversational Exchange 200in Graph 130 b. Conversation Participant's 50 a Conversational Activity210 or portion thereof from Round of Conversational Exchange 200 ka maybe found substantially similar with the highest similarity.Decision-making Unit 510 can play Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 200ka, thereby simulating AI Conversation Participant's 55 activity (i.e.silent body movements, etc.) during User's 60 Conversational Activity210 ia (i.e. speaking, etc.). Playing Sub-stream of Digital Pictures 145and Sub-stream of Digital Sound Samples 155 of ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 200 ka can start at any time during SubstantialSimilarity Comparisons 125 such as when an initial similarity is reachedas later described. Decision-making Unit 510 can then performSubstantial Similarity Comparisons 125 of User's 60 ConversationalActivity 2101 b (i.e. motionless silence, etc.) or portion thereof fromActivity Detector 160 with Conversation Participant's 50 aConversational Activities 210 or portions thereof from one or moreRounds of Conversational Exchange 200 in Graph 130 b interconnected withRound of Conversational Exchange 200 ka by outgoing Connections 853.Conversation Participant's 50 a Conversational Activity 210 or portionthereof from Round of Conversational Exchange 200 kb may be foundsubstantially similar. Decision-making Unit 510 may follow Connection853 q disregarding its less than highest weight, and play Sub-stream ofDigital Pictures 145 and Sub-stream of Digital Sound Samples 155 ofConversation Participant's 50 b Conversational Activity 210 from Roundof Conversational Exchange 200 kb, thereby simulating AI ConversationParticipant's 55 activity (i.e. speaking, etc.) during User's 60Conversational Activity 210 ib (i.e. motionless silence, etc.). PlayingSub-stream of Digital Pictures 145 and Sub-stream of Digital SoundSamples 155 of Conversation Participant's 50 b Conversational Activity210 from Round of Conversational Exchange 200 kb can start at any timeduring Substantial Similarity Comparisons 125 such as when an initialsimilarity is reached as later described. Decision-making Unit 510 canthen perform Substantial Similarity Comparisons 125 of User's 60Conversational Activity 210 ic (i.e. speaking, etc.) or portion thereoffrom Activity Detector 160 with Conversation Participant's 50 aConversational Activities 210 or portions thereof from one or moreRounds of Conversational Exchange 200 in Graph 130 b interconnected withRound of Conversational Exchange 200 kb by outgoing Connections 853.None of Conversation Participant's 50 a Conversational Activities 210 orportions thereof from one or more Rounds of Conversational Exchange 200interconnected with Round of Conversational Exchange 200 kb may be foundsubstantially similar. Decision-making Unit 510 may follow the highestweight Connection 853 r, and play Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 200kc, thereby simulating AI Conversation Participant's 55 activity (i.e.silent facial expressions, etc.) during User's 60 ConversationalActivity 210 ic (i.e. speaking, etc.). Playing Sub-stream of DigitalPictures 145 and Sub-stream of Digital Sound Samples 155 of ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 200 kc can start at any time during SubstantialSimilarity Comparisons 125 such as when a determination is made that aninitial similarity has not been reached as later described.Decision-making Unit 510 can then perform Substantial SimilarityComparisons 125 of User's 60 Conversational Activity 210 id (i.e. silentfacial expressions, etc.) or portion thereof from Activity Detector 160with Conversation Participant's 50 a Conversational Activities 210 orportions thereof from one or more Rounds of Conversational Exchange 200in Graph 130 b interconnected with Round of Conversational Exchange 200kc by outgoing Connections 853. Conversation Participant's 50 aConversational Activities 210 or portions thereof from multiple Roundsof Conversational Exchange 200 may be found substantially similar. Toenhance decision making (i.e. narrow down choices, etc.),Decision-making Unit 510 can also perform ancillary SubstantialSimilarity Comparisons 125 of User's 60 Conversational Activity 2101 c(i.e. speaking, etc.) or portion thereof from Activity Detector 160 withConversation Participant's 50 b Conversational Activities 210 (i.e.speaking, etc.) or portions thereof from the Rounds of ConversationalExchange 200 in Graph 130 b comprising substantially similarConversation Participant's 50 a Conversational Activities 210 orportions thereof. Conversation Participant's 50 b ConversationalActivity 210 or portion thereof from Round of Conversational Exchange200 kd may be found at least partially similar. Decision-making Unit 510may follow Connection 853 s, and play Sub-stream of Digital Pictures 145and Sub-stream of Digital Sound Samples 155 of ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 200 kd, thereby simulating AI ConversationParticipant's 55 activity (i.e. speaking, etc.) during User's 60Conversational Activity 210 id (i.e. silent facial expressions, etc.).Playing Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of Conversation Participant's 50 b ConversationalActivity 210 from Round of Conversational Exchange 200 kd can start atany time during Substantial Similarity Comparisons 125 such as when aninitial similarity is reached as later described. Decision-making Unit510 can then perform Substantial Similarity Comparisons 125 of User's 60Conversational Activity 210 ie (i.e. speaking, etc.) or portion thereoffrom Activity Detector 160 with Conversation Participant's 50 aConversational Activities 210 or portions thereof from one or moreRounds of Conversational Exchange 200 in Graph 130 b interconnected withRound of Conversational Exchange 200 kd by outgoing Connections 853.Conversation Participant's 50 a Conversational Activity 210 or portionthereof from Round of Conversational Exchange 200 ke may be foundsubstantially similar. Decision-making Unit 510 may follow Connection853 t, and play Sub-stream of Digital Pictures 145 and Sub-stream ofDigital Sound Samples 155 of Conversation Participant's 50 bConversational Activity 210 from Round of Conversational Exchange 200ke, thereby simulating AI Conversation Participant's 55 activity (i.e.motionless silence, etc.) during User's 60 Conversational Activity 210ie (i.e. speaking, etc.). Playing Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 200ke can start at any time during Substantial Similarity Comparisons 125such as when an initial similarity is reached as later described.Decision-making Unit 510 can implement similar logic or process for anyadditional Conversational Activities 210 from Activity Detector 160, andsoon.

The foregoing exemplary embodiment provides an example of utilizing acombination of Substantial Similarity Comparisons 125, ancillarycomparisons, weights of Connections 853, and/or other elements and/ortechniques. It should be understood that any of these elements and/ortechniques can be omitted, used in a different combination, or used incombination with other elements and/or techniques, in which case thepath of Rounds of Conversational Exchange 200 (or ConversationalActivities 210 therein) through Graph 130 b would be affectedaccordingly. Also, any of the elements and/or techniques utilized inother examples or embodiments described herein such as concurrentcomparisons, various arrangements of Conversational Activities 210 in aRound of Conversational Exchange 200, and/or others can similarly beutilized in this exemplary embodiment. One of ordinary skill in art willunderstand that this exemplary embodiment is described merely as anexample of a variety of possible implementations, and that while all ofits variations are too voluminous to describe, they are within the scopeof this disclosure.

Referring to FIG. 23, an exemplary embodiment of selecting a path ofRounds of Conversational Exchange 200 (or Conversational Activities 210therein) through Graph 130 b is illustrated. Graph 130 b may includeknowledge (i.e. interconnected Rounds of Conversational Exchange 200,etc.) of one or more conversations between Conversation Participants 50a and 50 b. In this example, Round of Conversational Exchange 200comprises a Conversational Activity 210 of Conversation Participant 50 acorrelated with Conversational Activity 210 of Conversation Participant50 b and a subsequent Conversational Activity 210 of ConversationParticipant 50 a correlated with a subsequent Conversational Activity210 of Conversation Participant 50 b similar to the one shown in FIG.6C. User 60 may be the same person as Conversation Participant 50 a orany other person. The conversation is simulated with AI ConversationParticipant 55 who uses knowledge of Conversation Participant 50 bstored in Graph 130 b to resemble Conversation Participant 50 b.Substantial Similarity Comparison 125 can be used to determinesubstantial similarity of the compared Conversational Activities 210 orportions thereof. Such substantial similarity, if achieved, may be usedprimarily for selecting a path through Graph 130 b, whereas, weight ofany Connection 853 and/or other elements may be used secondarily, forexample. In this example, as the simulated conversation progresses,Decision-making Unit 510 can be setup to receive User's 60 speakingConversational Activities 210 or portions thereof from Activity Detector160 while other Conversational Activities 210 from Activity Detector 160may be omitted.

For example, Decision-making Unit 510 can perform Substantial SimilarityComparisons 125 of User's 60 Conversational Activity 210 ja (i.e.speaking, etc.) or portion thereof from Activity Detector 160 withConversation Participant's 50 a Conversational Activities 210 orportions thereof from one or more Rounds of Conversational Exchange 200in Graph 130 b. Conversation Participant's 50 a Conversational Activity210 or portion thereof from Round of Conversational Exchange 2001 a maybe found substantially similar with highest similarity. Decision-makingUnit 510 may play Sub-stream of Digital Pictures 145 and Sub-stream ofDigital Sound Samples 155 of a correlated Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 2001a, thereby simulating AI Conversation Participant's 55 activity (i.e.silent facial expressions, etc.) during User's 60 ConversationalActivity 210 ja (i.e. speaking, etc.). Playing Sub-stream of DigitalPictures 145 and Sub-stream of Digital Sound Samples 155 of thecorrelated Conversation Participant's 50 b Conversational Activity 210from Round of Conversational Exchange 2001 a can start at any timeduring Substantial Similarity Comparisons 125 such as when an initialsimilarity is reached as later described. Decision-making Unit 510 mayalso play Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of a subsequent Conversation Participant's 50 bConversational Activity 210 from Round of Conversational Exchange 2001a, thereby simulating AI Conversation Participant's 55 activity (i.e.speaking, etc.) subsequent to User's 60 Conversational Activity 210 ja(i.e. speaking, etc.). Decision-making Unit 510 can then performSubstantial Similarity Comparisons 125 of User's 60 ConversationalActivity 210 jb (i.e. speaking, etc.) or portion thereof from ActivityDetector 160 with Conversation Participant's 50 a ConversationalActivities 210 (i.e. speaking, etc.) or portions thereof from one ormore Rounds of Conversational Exchange 200 in Graph 130 b interconnectedwith Round of Conversational Exchange 2001 a by outgoing Connections853. Conversation Participant's 50 a Conversational Activity 210 orportion thereof from Round of Conversational Exchange 2001 b may befound substantially similar with highest similarity. Decision-makingUnit 510 may follow Connection 853 v disregarding its less than highestweight, and play Sub-stream of Digital Pictures 145 and Sub-stream ofDigital Sound Samples 155 of a correlated Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 2001b, thereby simulating AI Conversation Participant's 55 activity (i.e.silent body movements, etc.) during User's 60 Conversational Activity210 jb (i.e. speaking, etc.), Playing Sub-stream of Digital Pictures 145and Sub-stream of Digital Sound Samples 155 of the correlatedConversation Participant's 50 b Conversational Activity 210 from Roundof Conversational Exchange 200 lb can start at any time duringSubstantial Similarity Comparisons 125 such as when an initialsimilarity is reached as later described. Decision-making Unit 510 mayalso play Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of a subsequent Conversation Participant's 50 bConversational Activity 210 from Round of Conversational Exchange 2001b, thereby simulating AI Conversation Participant's 55 activity (i.e.speaking, etc.) subsequent to User's 60 Conversational Activity 210 jb(i.e. speaking, etc.). Decision-making Unit 510 can then performSubstantial Similarity Comparisons 125 of User's 60 ConversationalActivity 210 jc (i.e. speaking, etc.) or portion thereof from ActivityDetector 160 with Conversation Participant's 50 a ConversationalActivities 210 (i.e. speaking, etc.) or portions thereof from one ormore Rounds of Conversational Exchange 200 in Graph 130 b interconnectedwith Round of Conversational Exchange 2001 b by outgoing Connections853. Conversation Participant's 50 a Conversational Activity 210 orportion thereof from Round of Conversational Exchange 2001 c may befound substantially similar with highest similarity, Decision-makingUnit 510 may follow Connection 853 w disregarding its less than highestweight, and play Sub-stream of Digital Pictures 145 and Sub-stream ofDigital Sound Samples 155 of a correlated Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 2001c, thereby simulating AI Conversation Participant's 55 activity (i.e.motionless silence, etc.) during User's 60 Conversational Activity 210jc (i.e. speaking, etc.). Playing Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of the correlated ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 2001 c can start at any time during SubstantialSimilarity Comparisons 125 such as when an initial similarity is reachedas later described, Decision-making Unit 510 may also play Sub-stream ofDigital Pictures 145 and Sub-stream of Digital Sound Samples 155 of asubsequent Conversation Participant's 50 b Conversational Activity 210from Round of Conversational Exchange 2001 c, thereby simulating AIConversation Participant's 55 activity (i.e. speaking, etc.) subsequentto User's 60 Conversational Activity 210 jc (i.e. speaking, etc.). SinceConnection 853 x is the only outgoing connection from Round ofConversational Exchange 2001 c, Decision-making Unit 510 may followConnection 853 x, and play Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of a correlated ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 2001 d, thereby simulating AI ConversationParticipant's 55 activity (i.e. silent body movements, etc.) duringUser's 60 Conversational Activity 210 jd (i.e. speaking, etc.).Decision-making Unit 510 may also play Sub-stream of Digital Pictures145 and Sub-stream of Digital Sound Samples 155 of a subsequentConversation Participant's 50 b Conversational Activity 210 from Roundof Conversational Exchange 2001 d, thereby simulating AI ConversationParticipant's 55 activity (i.e. speaking, etc.) subsequent to User's 60Conversational Activity 210 jd speaking, etc.). Decision-making Unit 510can then perform Substantial Similarity Comparisons 125 of User's 60Conversational Activity 210 je (i.e. speaking, etc.) or portion thereoffrom Activity Detector 160 with Conversation Participant's 50 aConversational Activities 210 (i.e. speaking, etc.) or portions thereoffrom one or more Rounds of Conversational Exchange 200 in Graph 130 binterconnected with Round of Conversational Exchange 2001 d by outgoingConnections 853. None of the Conversational Activities 210 or portionsthereof from one or more Rounds of Conversational Exchange 200 in Graph130 b interconnected with Round of Conversational Exchange 2001 d byoutgoing Connections 853 may be found substantially similar.Decision-making Unit 510 may follow the highest weight Connection 853 y,and play Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of a correlated Conversation Participant's 50 bConversational Activity 210 from Round of Conversational Exchange 2001e, thereby simulating AI Conversation Participant's 55 activity (i.e.motionless silence, etc.) during User's 60 Conversational Activity 210je (i.e. speaking, etc.). Playing Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of the correlated ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 2001 e can start at any time during SubstantialSimilarity Comparisons 125 such as when a determination is made that aninitial similarity has not been reached as later described.Decision-making Unit 510 may also play Sub-stream of Digital Pictures145 and Sub-stream of Digital Sound Samples 155 of a subsequentConversation Participant's 50 b Conversational Activity 210 from Roundof Conversational Exchange 2001 e, thereby simulating AI ConversationParticipant's 55 activity (i.e. speaking, etc.) subsequent to User's 60Conversational Activity 210 je (i.e. speaking, etc.). Decision-makingUnit 510 can implement similar logic or process for any additionalConversational Activities 210 from Activity Detector 160, and so on.

In both of the above described and/or other exemplary embodiments, anytime that substantial similarity or other similarity threshold is notachieved in any of the compared Conversational Activities 210 orportions thereof, instead of following the highest weight Connection 853or the only Connection 853, Decision-making Unit 510 can decide to lookfor a substantially or otherwise similar Conversational Activity 210 orportion thereof elsewhere in Graph 130 b.

In both of the above described and/or other exemplary embodiments, asthe simulated conversation progresses, a history (i.e. sequence, etc.)of User's 60 Conversational Activities 210 or portions thereof becomesavailable, which can then be collectively compared with ConversationalActivities 210 or portions thereof from Rounds of ConversationalExchange 200 in paths of Graph 130 b. Collectively comparingConversational Activities 210 or portions thereof may enableDecision-making Unit 510 to more accurately determine or anticipate AIConversation Participant's 55 activities in the simulated conversation.For example, Decision-making Unit 510 can perform collective SubstantialSimilarity Comparisons 125 of a history of User's 60 ConversationalActivities 210 or portions thereof from Activity Detector 160 withConversational Activities 210 or portions thereof from Rounds ofConversational Exchange 200 in one or more paths of Graph 130 b, Asadditional User's 60 Conversational Activities 210 or portions thereoffrom Activity Detector 160 become available, Decision-making Unit 510can use a longer history of User's 60 Conversational Activities 210 orportions thereof to compare with corresponding Conversational Activities210 or portions thereof from Rounds of Conversational Exchange 200 inpaths of Graph 130 b. In each cycle of comparisons, Decision-making Unit510 may choose the most similar of the compared paths and switch to amore suitable path based on such collective similarity determinations.

The foregoing exemplary embodiment provides an example of utilizing acombination of Substantial Similarity Comparisons 125, weights ofConnections 853, and/or other elements and/or techniques. It should beunderstood that any of these elements and/or techniques can be omitted,used in a different combination, or used in combination with otherelements and/or techniques, in which case the path of Rounds ofConversational Exchange 200 (or Conversational Activities 210 therein)through Graph 130 b would be affected accordingly. Also, any of theelements and/or techniques utilized in other examples or embodimentsdescribed herein such as ancillary comparisons, concurrent comparisons,various arrangements of Conversational Activities 210 in a Round ofConversational Exchange 200, and/or others can similarly be utilized inthis exemplary embodiment. One of ordinary skill in art will understandthat this exemplary embodiment is described merely as an example of avariety of possible implementations, and that while all of itsvariations are too voluminous to describe, they are within the scope ofthis disclosure.

Referring to FIG. 24, an exemplary embodiment of selecting a Sequence133 of Rounds of Conversational Exchange 200 (or ConversationalActivities 210 therein) in Collection of Sequences 130 c is illustrated.Collection of Sequences 130 c may include knowledge (i.e. Sequences 133of Rounds of Conversational Exchange 200, etc.) of one or moreconversations between Conversation Participants 50 a and 50 b. ASequence 133 may include Rounds of Conversational Exchange 200 of one ormore conversations or parts thereof. In this example, Round ofConversational Exchange 200 comprises a Conversational Activity 210 ofConversation Participant 50 a correlated with a Conversational Activity210 of Conversation Participant 50 b similar to the one shown in FIG.6A, User 60 may be the same person as Conversation Participant 50 a orany other person. The conversation is simulated with AI ConversationParticipant 55 who uses knowledge of Conversation Participant 50 bstored in Collection of Sequences 130 c to resemble ConversationParticipant 50 b. Collective substantial similarity of the comparedConversational Activities 210 or portions thereof, if achieved, may beused primarily for selecting a Sequence 133 in Collection of Sequences130 c, As the simulated conversation progresses, Decision-making Unit510 can receive User's 60 Conversational Activities 210 or portionsthereof from Activity Detector 160.

For example, Decision-making Unit 510 can perform Substantial SimilarityComparisons 125 of User's 60 Conversational Activity 2101 a (iespeaking, etc.) or portion thereof from Activity Detector 160 withConversation Participant's 50 a Conversational Activities 210 orportions thereof from corresponding Rounds of Conversational Exchange200 in Collection of Sequences 130 c. Conversation Participant's 50 aConversational Activity 210 or portion thereof from Round ofConversational Exchange 200 ma in Sequence 133 m may be foundsubstantially similar with highest similarity. Decision-making Unit 510may play Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of Conversation Participant's 50 b ConversationalActivity 210 from Round of Conversational Exchange 200 ma, therebysimulating AI Conversation Participant's 55 activity (i.e. silent bodymovements, etc.) during User's 60 Conversational Activity 210 ia (i.e.speaking, etc.). Playing Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 200ma can start at any time during Substantial Similarity Comparisons 125such as when an initial similarity is reached as later described.Decision-making Unit 510 can then perform collective SubstantialSimilarity Comparisons 125 of User's 60 Conversational Activities 210ia-210 ib or portions thereof from Activity Detector 160 withConversation Participant's 50 a Conversational Activities 210 orportions thereof from corresponding Rounds of Conversational Exchange200 in Collection of Sequences 130 c, Conversation Participant's 50 aConversational Activities 210 or portions thereof from Rounds ofConversational Exchange 200 ma-200 mb in Sequence 133 m may be foundsubstantially similar with highest similarity. Decision-making Unit 510may play Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of Conversation Participant's 50 b ConversationalActivity 210 from Round of Conversational Exchange 200 mb, therebysimulating AI Conversation Participant's 55 activity (i.e. speaking,etc.) during User's 60 Conversational Activity 210 ib (i.e. motionlesssilence, etc.). Playing Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 200mb can start at any time during Substantial Similarity Comparisons 125such as when an initial similarity is reached as later described.Decision-making Unit 510 can then perform collective SubstantialSimilarity Comparisons 125 of User's 60 Conversational Activities 210ia-210 ic or portions thereof from Activity Detector 160 withConversation Participant's 50 a Conversational Activities 210 orportions thereof from corresponding Rounds of Conversational Exchange200 in Collection of Sequences 130 c, Conversation Participant's 50 aConversational Activities 210 or portions thereof from Rounds ofConversational Exchange 200 na-200 nc in Sequence 133 n may be foundsubstantially similar with highest similarity. Decision-making Unit 510may play Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of Conversation Participant's 50 b ConversationalActivity 210 from Round of Conversational Exchange 200 nc, therebysimulating AI Conversation Participant's 55 activity (i.e. silent facialexpressions, etc.) during User's 60 Conversational Activity 210 ic (i.e.speaking, etc.). Playing Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 200nc can start at any time during Substantial Similarity Comparisons 125such as when an initial similarity is reached as later described,Decision-making Unit 510 can then perform collective SubstantialSimilarity Comparisons 125 of User's 60 Conversational Activities 2101a-210 id or portions thereof from Activity Detector 160 withConversation Participant's 50 a Conversational Activities 210 orportions thereof from corresponding Rounds of Conversational Exchange200 in Collection of Sequences 130 c. Conversation Participant's 50 aConversational Activities 210 or portions thereof from Rounds ofConversational Exchange 200 na-200 nd in Sequence 133 n may be foundsubstantially similar with highest similarity. Decision-making Unit 510may play Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of Conversation Participant's 50 b ConversationalActivity 210 from Round of Conversational Exchange 200 nd, therebysimulating AI Conversation Participant's 55 activity (i.e. speaking,etc.) during User's 60 Conversational Activity 210 id (i.e. silentfacial expressions, etc.). Playing Sub-stream of Digital Pictures 145and Sub-stream of Digital Sound Samples 155 of ConversationParticipant's 50 b Conversational Activity 210 from Round ofConversational Exchange 200 nd can start at any time during SubstantialSimilarity Comparisons 125 such as when an initial similarity is reachedas later described. Decision-making Unit 510 can then perform collectiveSubstantial Similarity Comparisons 125 of User's 60 ConversationalActivities 210 ia-210 ie or portions thereof from Activity Detector 160with Conversation Participant's 50 a Conversational Activities 210 orportions thereof from corresponding Rounds of Conversational Exchange200 in Collection of Sequences 130 c. Conversation Participant's 50 aConversational Activities 210 or portions thereof from Rounds ofConversational Exchange 200 na-200 ne in Sequence 133 n may be foundsubstantially similar with highest similarity. Decision-making Unit 510may play Sub-stream of Digital Pictures 145 and Sub-stream of DigitalSound Samples 155 of Conversation Participant's 50 b ConversationalActivity 210 from Round of Conversational Exchange 200 ne, therebysimulating AI Conversation Participant's 55 activity (i.e. silent facialexpressions, etc.) during User's 60 Conversational Activity 210 ie (i.e.speaking, etc.). Playing Sub-stream of Digital Pictures 145 andSub-stream of Digital Sound Samples 155 of Conversation Participant's 50b Conversational Activity 210 from Round of Conversational Exchange 200ne can start at any time during Substantial Similarity Comparisons 125such as when an initial similarity is reached as later described.Decision-making Unit 510 can implement similar logic or process for anyadditional Conversational Activities 210 from Activity Detector 160, andso on.

In some embodiments, various elements and/or techniques can be utilizedin the aforementioned collective substantial similarity determinations.In some aspects, collective substantial similarity of the comparedConversational Activities 210 can be determined based on similarities orsimilarity indexes of the individually compared ConversationalActivities 210. For example, an average or weighted average ofsimilarities or similarity indexes of individually comparedConversational Activities 210 can be used to determine collectivesimilarity of the compared Conversational Activities 210. For instance,to affect the weighting of the collective similarity, a higher weight orimportance (i.e. importance index, etc.) can be assigned to thesimilarities or similarity indexes of the current and/or recentConversational Activities 210 and decreased for ConversationalActivities 210 in the past. In another instance, a higher weight orimportance (i.e. importance index, etc.) can be assigned to thesimilarities or similarity indexes of speaking Conversational Activities210 while lower weight or importance (i.e. importance index, etc.) canbe assigned to the similarities or similarity indexes of observingConversational Activities 210 (i.e. silent facial expressions, silentbody movements, motionless silence, etc.). Any other higher or lowerimportance assignment can be implemented. In other aspects, collectivesubstantial similarity of the compared Conversational Activities 210 canbe determined based on similarities or similarity indexes of Sub-streamsof Digital Pictures 145 and/or Sub-streams of Digital Sound Samples 155in the compared Conversational Activities 210. For example, an averageor weighted average of similarities or similarity indexes of some or allSub-streams of Digital Pictures 145 and/or Sub-streams of Digital SoundSamples 155 of the compared Conversational Activities 210 can be used todetermine collective similarity of the compared ConversationalActivities 210. For instance, to affect the weighting of the collectivesimilarity, a higher weight or importance (i.e. importance index, etc.)can be assigned to the similarities or similarity indexes of Sub-streamsof Digital Pictures 145 and/or Sub-streams of Digital Sound Samples 155in the current and/or recent Conversational Activities 210 and decreasedfor Conversational Activities 210 in the past. In another instance, ahigher weight or importance (i.e. importance index, etc.) can beassigned to similarities or similarity indexes of Sub-streams of DigitalPictures 145 and/or Sub-streams of Digital Sound Samples 155 in speakingConversational Activities 210 while lower weight or importance (i.e.importance index, etc.) can be assigned to similarities or similarityindexes of Sub-streams of Digital Pictures 145 and/or Sub-streams ofDigital Sound Samples 155 in observing Conversational Activities 210(i.e. silent facial expressions, silent body movements, motionlesssilence, etc.). Any other higher or lower importance assignment can beimplemented. In further aspects, collective substantial similarity ofthe compared Conversational Activities 210 can be determined based onsimilarity of some or all words, features, sound samples, and/or otherelements of Sub-stream of Digital Sound Samples 155 in the comparedConversational Activities 210, In further aspects, collectivesubstantial similarity of the compared Conversational Activities 210 canbe determined based on similarity of some or all pictures (i.e. frames,etc.), features, regions, pixels, and/or other elements of Sub-stream ofDigital Pictures 145 in the compared Conversational Activities 210. Athreshold for collective substantial similarity can be utilized with anyof the aforementioned elements and/or techniques. For example,collective substantial similarity of the compared ConversationalActivities 210 can be achieved if collective similarity of theirelements exceeds a threshold. Such threshold can be defined by a user,by AIIM system administrator, or automatically by the system based onexperience, testing, inquiry, analysis, synthesis, or other techniques,knowledge, or input. Any combination of the previously describedcollective substantial similarity determinations or calculations can beutilized in alternate embodiments. Any other elements and/or techniquescan be utilized to determine or calculate collective substantialsimilarity in alternate embodiments. Similar elements and/or techniquesas the aforementioned can be used for collective similaritydeterminations of other compared elements such as Rounds ofConversational Exchange 200 and/or others. Collective similaritydeterminations may include any features, functionalities, andembodiments of Substantial Similarity Comparison 125.

In other embodiments, Collection of Sequences 130 c may enable User 60to manually choose a particular conversation or part thereof containedin a Sequence 133. For example, Collection of Sequences 130 c can beshown via a graphical user interface (GUI) and User 60 may select aparticular Sequence 133 to implement. The system can simulate aconversation or part thereof contained in the selected Sequence 133 andenable User 60 to recall his/her memories of that particularconversation.

The foregoing exemplary embodiment provides an example of utilizingcollective substantial similarity determinations and/or other elementsor techniques. It should be understood that any of these elements and/ortechniques can be omitted, used in a different combination, or used incombination with other elements and/or techniques, in which case thechoice of Sequence 133 in Collection of Sequences 130 c would beaffected accordingly. Also, any of the elements and/or techniquesutilized in other examples or embodiments described herein such asindividual Substantial Similarity Comparisons 125, ancillarycomparisons, concurrent comparisons, various arrangements ofConversational Activities 210 in a Round of Conversational Exchange 200,and/or others can similarly be utilized in this exemplary embodiment.One of ordinary skill in art will understand that this exemplaryembodiment is described merely as an example of a variety of possibleimplementations, and that while all of its variations are too voluminousto describe, they are within the scope of this disclosure.

Referring to FIG. 25, an exemplary embodiment of selecting Rounds ofConversational Exchange 200 (or Conversational Activities 210 therein)in a single Sequence 133 is illustrated. The single Sequence 133 mayinclude knowledge (i.e. Rounds of Conversational Exchange 200, etc.) ofone or more conversations or portions thereof between ConversationParticipants 50 a and 50 b. In this example, Round of ConversationalExchange 200 comprises a Conversational Activity 210 of ConversationParticipant 50 a correlated with a Conversational Activity 210 ofConversation Participant 50 b similar to the one shown in FIG. 6A. User60 may be the same person as Conversation Participant 50 a or any otherperson. The conversation is simulated with AI Conversation Participant55 who uses knowledge of Conversation Participant 50 b stored in singleSequence 133 to resemble Conversation Participant 50 b. Individual orcollective substantial similarity of the compared ConversationalActivities 210 or portions thereof, if achieved, may be used primarilyfor selecting Rounds of Conversational Exchange 200 in Sequence 133. Asthe simulated conversation progresses, Decision-making Unit 510 canreceive User's 60 Conversational Activities 210 or portions thereof fromActivity Detector 160.

In some aspects, Decision-making Unit 510 can perform the previouslydescribed individual Substantial Similarity Comparisons 125 of User's 60Conversational Activities 210 or portions thereof from Activity Detector160 with Conversational Activities 210 or portions thereof from Roundsof Conversational Exchange 200 in Sequence 133, Such individualSubstantial Similarity Comparisons 125 can be performed by traversingSequence 133, For example, Decision-making Unit 510 can performindividual Substantial Similarity Comparisons 125 of User's 60Conversational Activity 210 or portion thereof from Activity Detector160 with Conversational Activities 210 or portions thereof from Roundsof Conversational Exchange 200 of Sequence 133 in incremental or othertraversing pattern. The incremental traversing may start from one end ofSequence 133 and move the comparison up or down one (i.e. or any amount,etc.) incremental Conversational Activity 210 at a time. Othertraversing patterns or methods can be employed such as starting from themiddle of the Sequence 133 and subdividing the resulting sub-sequencesin a recursive pattern, or any other traversing pattern or method.

In other aspects, Decision-making Unit 510 can perform the previouslydescribed collective Substantial Similarity Comparisons 125 of a history(i.e. a sequence itself, etc.) of User's 60 Conversational Activities210 or portions thereof from Activity Detector 160 with ConversationalActivities 210 or portions thereof from Rounds of ConversationalExchange 200 in subsequences of Sequence 133. Such collectiveSubstantial Similarity Comparisons 125 can be performed by traversingSequence 133. For example, Decision-making Unit 510 can performcollective Substantial Similarity Comparisons 125 of a history of User's60 Conversational Activities 210 or portions thereof from ActivityDetector 160 with Conversational Activities 210 or portions thereof fromRounds of Conversational Exchange 200 in subsequences of Sequence 133 inthe previously described incremental, recursive, or other traversingpattern. As additional User's 60 Conversational Activities 210 orportions thereof from Activity Detector 160 become available,Decision-making Unit 510 can use a longer history of User's 60Conversational Activities 210 or portions thereof to compare withConversational Activities 210 or portions thereof from Rounds ofConversational Exchange 200 in subsequences of Sequence 133. In eachcycle of comparisons, Decision-making Unit 510 may choose the mostsimilar of the compared subsequences and switch to a more suitablesubsequence based on such collective similarity determinations.

In some designs, a Round of Conversational Exchange 200 can be connectednot only with a next Round of Conversational Exchange 200 in Sequence133, but also with any other Round of Conversational Exchange 200,thereby creating alternate routes or shortcuts through Sequence 133, Anynumber of Connections 853 connecting any Rounds of ConversationalExchange 200 in Sequence 133 can be utilized. In such implementations,Decision-making Unit 510 can perform Substantial Similarity Comparisons125 of User's 60 Conversational Activity 210 or portion thereof fromActivity Detector 160 with Conversational Activities 210 or portionsthereof from Rounds of Conversational Exchange 200 in Sequence 133interconnected with prior Round of Conversational Exchange 200 byoutgoing Connections 853, for example. Decision-making Unit 510 can thenfollow a Connection 853 based on similarity determinations as previouslydescribed, in alternate designs, Connections 853 can be optionallyomitted from a Sequence 133 that does not include shortcuts.

In both of the above described and/or other exemplary embodiments, anytime that substantial similarity or other similarity threshold is notachieved in one or more of the compared Conversational Activities 210 orportions thereof, Decision-making Unit 510 can decide to look for one ormore substantially or otherwise similar Conversational Activities 210 orportions thereof in any one or more Sequences 133.

The foregoing exemplary embodiment provides an example of utilizingindividual or collective substantial similarity determinations and/orother elements or techniques. It should be understood that any of theseelements and/or techniques can be omitted, used in a differentcombination, or used in combination with other elements and/ortechniques, in which case the choice of Sequence 133 or Rounds ofConversational Exchange 200 (or Conversational Activities 210 therein)in a Sequence 133 would be affected accordingly. Also, any of theelements and/or techniques utilized in other examples or embodimentsdescribed herein such as ancillary comparisons, concurrent comparisons,various arrangements of Conversational Activities 210 in a Round ofConversational Exchange 200, and/or others can similarly be utilized inthis exemplary embodiment. One of ordinary skill in art will understandthat this exemplary embodiment is described merely as an example of avariety of possible implementations, and that while all of itsvariations are too voluminous to describe, they are within the scope ofthis disclosure.

Referring now to the aforementioned initial similarity determinations,in some embodiments, it may be desirable to implement an AI ConversationParticipant's 55 activity soon or immediately after User 60 startshis/her corresponding Conversational Activity 210. Decision-making Unit510 does not need to wait to receive an entire User's 60 ConversationalActivity 210 in order to compare it with Conversational Activities 210from Rounds of Conversation Exchange 200 stored in Knowledgebase 130,Neural Network 130 a, Graph 130 b, Collection of Sequences 130 c,Sequence 133, and/or other data structure, knowledge structure, orrepository. Instead, Decision-making Unit 510 can perform SubstantialSimilarity Comparisons 125 of portions of Conversational Activities 210to determine initial similarity at any time while User 60 performshis/her Conversational Activity 210. Such portions of ConversationalActivities 210 may include pictures (i.e. frames, etc.), features,regions, pixels, or other elements of Sub-streams of Digital Pictures145 and/or words, features, sound samples, or other elements ofSub-streams of Digital Sound Samples 155 included in the ConversationalActivities 210. For example, to determine initial similarity,Decision-making Unit 510 can utilize incoming words as they are receivedfrom User 60 in real time. Specifically, in this example, as initial oneor more words come from User 60, Decision-making Unit 510 can performSubstantial Similarity Comparisons 125 of these words with words ofConversation Participant's 50 a Conversational Activities 210 from oneor more Rounds of Conversational Exchange 200 stored in Knowledgebase130, Neural Network 130 a, Graph 130 b, Collection of Sequences 130 c,Sequence 133, and/or other data structure, knowledge structure, orrepository. If a threshold for initial similarity is not achieved,Substantial Similarity Comparison 125 can use an additional word comingfrom User 60 to determine initial similarity. If a threshold for initialsimilarity is still not achieved, Substantial Similarity Comparison 125can use additional incoming words, thereby further increasing the numberof words used in the initial similarity determination until initialsimilarity is achieved. Similar logic or process for determining initialsimilarity can be implemented with incoming sound features, incomingsound samples, incoming pictures (i.e. frames, etc.), incoming regionsof pixels, incoming picture features, incoming pixels, and/or otherelements of Sub-stream of Digital Sound Samples 155 and/or Sub-stream ofDigital Pictures 145. At any point when initial similarity is determinedfor any of the compared Conversational Activities 210 or portionsthereof, Decision-making Unit 510 can play Sub-stream of DigitalPictures 145 and Sub-stream of Digital Sound Samples 155 of aConversational Activity 210 correlated with the initially similarConversational Activity 210, thereby simulating AI ConversationParticipant's 55 activity during User's 60 corresponding ConversationalActivity 210. Therefore, initial similarity determination enablesquickly determining a best guess of Conversational Activity 210 to usefor simulating AI Conversation Participant 55, Decision-making Unit 510can switch from an initially similar Conversational Activity 210 to abetter Conversational Activity 210 if a better initial, substantial, orother similarity is determined. For example, after initial similarity isdetermined for a Conversational Activity 210, Decision-making Unit 510can continue performing Substantial Similarity Comparisons 125 inattempt to find even better initial or other similarity in otherConversational Activities 210, and if found, Decision-making Unit 510can switch to the more similar Conversational Activity 210 forsimulating AI Conversation Participant 55. In some aspects,Decision-making Unit 510 may adjust the length of Sub-stream of DigitalPictures 145 and Sub-stream of Digital Sound Samples 155 of theinitially similar Conversational Activity 210 to synchronize theirplaying with User's 60 Conversational Activity 210. In other aspects, ifinitial similarity is not achieved after a threshold number of attemptsor threshold period of time, Decision-making Unit 510 can use weights ofConnections 853 and/or alternative elements or techniques in selectingConversational Activity 210 for simulating AI Conversation Participant55.

Referring to some embodiments of System for Using AIIMs 500, in caseswhere Decision-making Unit 510 does not find a substantially orotherwise acceptably similar Conversational Activity 210 in any of theconsidered Rounds of Conversational Exchange 200 stored in Knowledgebase130, Neural Network 130 a, Graph 130 b, Collection of Sequences 130 c,Sequence 133, and/or other data structure, knowledge structure, orrepository, Decision-making Unit 510 can utilize various techniquesinstead of or in addition to Connections 853 in selecting aconversational path. In some aspects, Decision-making Unit 510 can playa message such as “I did not understand that”, “what was that”, “I donot recall that”, or other message that offers User 60 a chance to alterthe path of conversation by inputting another Conversational Activity210. The redirecting message itself can be learned by System forLearning AIIMs 100 through the previously described learning process ofconversations. One or more such learned redirecting messages can bestored in a special repository dedicated to conversation redirectingfunctionalities. A redirecting message may include one or moreConversational Activities 210 and/or other elements. In one example,System for Learning AIIMs 100 may include a list of redirecting phrasesor messages to look for such as “what was that”, “I did not understandthat”, “I don't recall that”, or other messages, which when detected ina conversation may be learned by System for Learning AIIMs 100 andstored in the special repository comprising redirecting messages. Inanother example, the system may ask Conversation Participant 50 to speakredirecting messages, which System for Learning AIIMs 100 may learn andstore in the special repository. Such “training session” may beperformed during system configuration or at any time convenient for theuser. In other aspects, instead of offering User 60 a chance to alterthe path of conversation as aforementioned, Decision-making Unit 510itself may attempt to redirect the conversation by playing a redirectingmessage such as “let's talk about”, “let me tell you”, “you know”, orother message, after which, Decision-making Unit 510 can direct User 60into an alternate conversational path (i.e. follow highest weight orother Connection 853, etc.). Such redirecting message can be learnedautomatically or through training as previously described. In furtheraspects, Decision-making Unit 510 can present User 60 with a map orother representation of conversational paths comprising Rounds ofConversational Exchange 200 and/or Conversational Activities 210 whereUser 60 can manually choose which conversational path to pursue. User 60can choose by clicking on paths or on individual Rounds ofConversational Exchange 200 and/or Conversational Activities 210 in agraphical or other interface.

Referring to some embodiments of System for Using AIIMs 500, in caseswhere Decision-making Unit 510 runs out of conversational path,Decision-making Unit 510 can utilize various techniques in redirectingto a new conversational path. In some aspects, Decision-making Unit 510can play a message such as “what else would you like to talk about”,“what else interests you”, “how about we talk about something else”, orother message that offers User 60 a chance to continue the simulatedconversation by inputting another Conversational Activity 210, In otheraspects, instead of offering User 60 a chance to continue the simulatedconversation in a path that User 60 wants, Decision-making Unit 510itself may attempt to redirect the conversation by playing a redirectingmessage such as “let's talk about”, “let me tell you”, “you know”, orother message, after which, Decision-making Unit 510 can direct User 60into a new conversational path as previously described. Any of theaforementioned redirecting messages can be learned automatically orthrough training as previously described. In further aspects,Decision-making Unit 510 can present User 60 with a map or otherrepresentation of conversational paths comprising Rounds ofConversational Exchange 200 and/or Conversational Activities 210 whereUser 60 can manually choose which new conversational path to pursue aspreviously described.

Referring to some embodiments of System for Using AIIMs 500, in caseswhere Decision-making Unit 510 needs to use Connections 853 (i.e. ifsubstantially or otherwise similar Conversational Activity 210 is notfound, etc.) in a conversational path, Decision-making Unit 510 can attimes decide to follow a random Connection 853 instead of following thehighest weight Connection 853. Following a random Connection 853 mayavoid a potential issue of one or more Connections 853 becoming sofrequent and dominant that they would not allow alternative paths to beconsidered or selected.

Referring to some embodiments of System for Using AIIMs 500, in caseswhere User 60 starts speaking while AI Conversation Participant 55speaks (i.e. performs a speaking activity, etc.), AI ConversationParticipant's 55 speaking can be interrupted and the process of findinga Conversational Activity 210 that is substantially or otherwise similarto User's 60 speaking Conversational Activity 210 and implementing acorrelated Conversational Activity 210 by AI Conversation Participant 55can restart as previously described. The system can therefore givepriority to User 60 over AI Conversation Participant 55 in a simulatedconversation.

Referring to some embodiments of System for Using AIIMs 500,Decision-making Unit 510 can use various transitioning techniques toswitch from one Conversational Activity 210 to another. For example,such transitioning can be implemented when one AI ConversationParticipant's 55 Conversational Activity 210 ends and another starts,Transitioning among Conversational Activities 210 enables a simulatedconversation to be perceived as smooth or uninterrupted, therebyenhancing User 60 experience. Transitioning mostly relates to visualappearance of AI Conversation Participant 55 in a simulatedconversation, although, sound transitioning can also be implemented.Transitioning may include a seamless visual blending of AI ConversationParticipant 55 in the last picture of a preceding Sub-stream of DigitalPictures 145 and AI Conversation Participant 55 in the first picture ofa subsequent Sub-stream of Digital Pictures 145. In some aspects,transitioning includes moving, centering, aligning, resizing, and/orotherwise transforming AI Conversation Participant's 55 figure (i.e.face, upper body, etc.) or picture within which AI ConversationParticipant's 55 figure resides. In one example, AI ConversationParticipant's 55 figure can be centered on screen throughout thesimulated conversation to smooth the positioning aspect of thetransition. Any other moving or aligning can similarly be implemented.In another example, AI Conversation Participant's 55 figure can beresized to a certain size throughout the simulated conversation tosmooth the size aspect of the transition. The size can be defined by auser, by AIN system administrator, or automatically by the system. Anyof the previously described resizing and/or other transforming can beutilized in the transitioning. In other aspects, transitioning includeslighting or color adjustments of AI Conversation Participant's 55 figureor picture within which AI Conversation Participant's 55 figure resides.In one example, a certain level or balance of lighting or color can bemaintained for AI Conversation Participant's 55 figure throughout thesimulated conversation to smooth the lighting or color aspect of thetransition. In another example, AI Conversation Participant's 55figure's lighting or color can be adjusted to better resemble AIConversation Participant's 55 figure in a preceding Sub-stream ofDigital Pictures 145. Any of the previously described lighting or coloradjustments can be utilized in the transitioning. In further aspects,transitioning includes a cut, dissolve, and/or other motion pictureediting techniques suitable for transitioning between motion pictures.In one example, a cut can be used to switch instantly from oneSub-stream of Digital Pictures 145 to another without any pictureprocessing. In another example, a dissolve (i.e. cross-dissolve, etc.)can be used to gradually transition from one Sub-stream of DigitalPictures 145 to another. In other aspects, transitioning includesmorphing and/or other transformations of AI Conversation Participant's55 figure or picture within which AI Conversation Participant's 55figure resides. Morphing may involve the steps of warping andcross-dissolving in some implementations. Specifically, morphingcomprises defining corresponding points on two pictures and distortingone picture into the other as they cross-dissolve. Defining thecorresponding points on the pictures can be performed automaticallyusing picture or facial recognition techniques that can detect corners,blobs, and/or other points of interest on a picture as previouslydescribed. Any features, functionalities, and embodiments of PictureRecognizer 163 can be used in transitioning or morphing. In one example,one AI Conversation Participant's 55 figure can be morphed into anotherby detecting significant points such as the contour of the nose,locations of eyes, corners of the mouth, and/or other facial or bodypoints on both AI Conversation Participant's 55 figures, Morphing canthen distort the first AI Conversation Participant's 55 figure into theshape of the second AI Conversation Participant's 55 figure whilecross-dissolving the two AI Conversation Participant's 55 figures. Beierand Neely, and/or other algorithm can be used to compute thetransformation of image coordinates required for the distortion orwarping. Other morphing or warping techniques can be used such asmesh/grid-based warping, feature-based morphing, and/or others. One ofordinary skill in art will understand that the aforementionedtransitioning techniques are described merely as examples of a varietyof possible implementations, and that while all possible transitioningtechniques are too voluminous to describe, other transitioningtechniques known in art are within the scope of this disclosure.

Referring to some embodiments of System for Using AIIMs 500,Decision-making Unit 510 can use various bridging techniques to fill agap between Conversational Activities 210. For example, such bridgingcan be implemented when a next AI Conversation Participant's 55Conversational Activity 210 is not yet known or missing. Bridging amongConversational Activities 210 enables a simulated conversation to beperceived as smooth or uninterrupted, thereby enhancing User 60experience. Bridging mostly relates to visual appearance of AIConversation Participant 55 in a simulated conversation, although, soundbridging can also be implemented. Bridging may include generating orcreating intermediate pictures (i.e. frames, etc.) between twoSub-streams of Digital Pictures 145 to give the appearance that AIConversation Participant 55 in a preceding Sub-stream of DigitalPictures 145 evolves smoothly into AI Conversation Participant 55 in asubsequent Sub-stream of Digital Pictures 145. Any features,functionalities, and embodiments of the previously describedtransitioning can be used in bridging. In some aspects, bridgingincludes interpolation, inbetweening, extrapolation, and/or otherpicture or frame generation technique. In one example, interpolationand/or inbetweening can be used to generate intermediate pictures (i.e.frames, etc.) between the last picture of a preceding Sub-stream ofDigital Pictures 145 and the first picture of a subsequent Sub-stream ofDigital Pictures 145. In other aspects, bridging includes playing orreplaying one or more Sub-streams of Digital Pictures 145 or portionsthereof. In one example, a simple way to bridge between Sub-streams ofDigital Pictures 145 is to repeatedly replay or freeze the last picture(i.e. frame, etc.) of a preceding Sub-stream of Digital Pictures 145until a subsequent Sub-stream of Digital Pictures 145 is known. Thisapproach can be used in any implementation, but may provide realisticbridging for short duration gaps. In another example, a portion (i.e.certain number of rearmost pictures, etc.) of a preceding Sub-stream ofDigital Pictures 145 can be repeatedly replayed until a subsequentSub-stream of Digital Pictures 145 is known. In a further example, theentire preceding Sub-stream of Digital Pictures 145 can be repeatedlyreplayed until a subsequent Sub-stream of Digital Pictures 145 is known.In a further example, any one or more Sub-streams of Digital Pictures145 or portions (i.e. certain number of pictures, etc.) thereof can beplayed or repeatedly replayed until a subsequent Sub-stream of DigitalPictures 145 is known. In such implementations, one or more Sub-streamsof Digital Pictures 145 from a similar Conversational Activity 210 maybe best suited to play or replay. For instance, if bridging is neededbetween an observing Conversational Activity 210 (i.e. silent facialexpressions, silent body movements, motionless silence, etc.) and aspeaking Conversational Activity 210, a Sub-stream of Digital Pictures145 from another observing Conversational Activity 210, preferably ofthe same type, can be played or replayed until the speakingConversational Activity 210 is known. One of ordinary skill in art willunderstand that the aforementioned bridging techniques are describedmerely as examples of a variety of possible implementations, and thatwhile all possible bridging techniques are too voluminous to describe,other bridging techniques known in art are within the scope of thisdisclosure.

Referring to FIG. 26, the illustration shows an embodiment of a method6200 for using AIIMs. The method can be used on a computing device orsystem to enable simulating a conversation with an artificiallyintelligent conversation participant. The computing device or system mayinclude a user device (i.e. User Device 80, etc.), a server (i.e. Server90, etc.), a dedicated device, a host device (i.e. Host Device 98, etc.)or an embedded element thereof, and/or others. Method 6200 may includeany action or operation of any of the disclosed methods such as method6100 and/or others. Other additional steps, actions, or operations canbe included as needed, or some of the disclosed ones can be optionallyomitted, or a different combination or order thereof can be implementedin alternate embodiments of method 6200.

At step 6205, a stored plurality of rounds of conversational exchangeincluding a first round of conversational exchange are accessed, thefirst round of conversational exchange comprising a recording of a firstconversation participant's first conversational activity and a recordingof a second conversation participant's first conversational activity.The stored plurality of rounds of conversational exchange comprise anyfeatures, functionalities, and embodiments of the stored plurality ofrounds of conversational exchange described in steps 6135 and/or 6140 ofmethod 6100 as applicable.

At step 6210, a stream of digital pictures of a user is captured. Step6210 may include any action or operation described in step 6105 ofmethod 6100 as applicable.

At step 6215, a stream of digital sound samples of the user is captured.Step 6215 may include any action or operation described in step 6110 ofmethod 6100 as applicable.

At step 6220, the user's first conversational activity is detected fromat least one of the stream of digital pictures of the user or the streamof digital sound samples of the user, Step 6220 may include any actionor operation described in step 6125 of method 6100 as applicable.

At step 6225, at least one portion of a recording of the user's firstconversational activity are compared with at least one portion of therecording of the first conversation participant's first conversationalactivity. A portion of a recording of a conversational activity (i.e.Conversational Activity 210, etc.) may include sub-stream of digitalsound samples (i.e. Sub-stream of Digital Sound Samples 155, etc.) orportion (i.e. word, feature, sound sample, etc.) thereof, A portion of arecording of a conversational activity may include sub-stream of digitalpictures (i.e. Sub-stream of Digital Pictures 145, etc.) or portion(i.e. picture, feature, region of pixels, pixel, etc.) thereof. In someembodiments, the comparing may include comparing sub-stream of digitalsound samples or portions thereof of one recording of conversationalactivity with sub-stream of digital sound samples or portions thereof ofanother recording of conversational activity. In some aspects, thecomparing may include comparing one or more words recognized from onesub-stream of digital sound samples with one or more words recognizedfrom another sub-stream of digital sound samples. In other aspects, thecomparing may include comparing one or more features (i.e. soundfeatures, etc.) from one sub-stream of digital sound samples with one ormore sound features (i.e. sound features, etc.) from another sub-streamof digital sound samples. In further aspects, the comparing may includecomparing sound samples from one sub-stream of digital sound sampleswith sound samples from another sub-stream of digital sound samples. Infurther aspects, Dynamic Time Warping (DTW) and/or other adjustments ortechniques can be utilized for comparing and/or aligning temporalsequences (i.e. sub-streams of digital sound samples, etc.) that mayvary in time or speed. Comparing may also include other aspects orproperties of digital sound or sound samples examples of which compriseamplitude adjustment, sample rate or frequency adjustment, noisereduction, and/or others. In other embodiments, the comparing mayinclude comparing sub-stream of digital pictures or portions thereof ofone recording of conversational activity with sub-stream of digitalpictures or portions thereof of another recording of conversationalactivity. In some designs, Dynamic Time Warping (DTW) and/or otheradjustments or techniques can be utilized for comparison and/or aligningtemporal sequences (i.e. sub-streams of digital pictures, etc.) that mayvary in time or speed. In some aspects, the comparing may includecomparing pictures from one sub-stream of digital pictures with picturesfrom another sub-stream of digital pictures. In some aspects, comparingof individual pictures (i.e. pictures from the sub-streams of digitalpictures, etc.) may include comparing one or more features (i.e. picturefeatures, etc.) of one picture with one or more features (i.e. picturefeatures, etc.) of another picture. In other aspects, comparing ofindividual pictures may include comparing regions of pixels of onepicture with regions of pixels of another picture. In further aspects,comparing of individual pictures may include comparing pixels of onepicture with pixels of another picture. Comparing may also include otheraspects or properties of digital pictures or pixels examples of whichcomprise color adjustment, size adjustment, transparency (i.e. alphachannel, etc.), use of a mask, and/or others. Any combination of theaforementioned and/or other elements or techniques can be utilized inalternate embodiments of the comparing. Comparing comprises any actionor operation by or for a Decision-making Unit 510, SubstantialSimilarity Comparison 125, and/or other disclosed elements.

At step 6230, a determination is made that a similarity between at leastone portion of the recording of the user's first conversational activityand at least one portion of the recording of the first conversationparticipant's first conversational activity exceeds a similaritythreshold. In some embodiments, the determination may includedetermining that the number or percentage of matching or substantiallymatching portions of sub-streams of digital pictures and/or portions ofsub-streams of digital sound samples of the compared recordings ofconversational activities exceeds a threshold number or thresholdpercentage. In some aspects, weight can be assigned to sub-streams ofdigital pictures or portions thereof and/or sub-streams of digital soundsamples or portions thereof indicating their importance in thesimilarity determination. In other embodiments, the determination mayinclude determining that the number or percentage of matching wordsrecognized from the sub-streams of digital sound samples exceeds athreshold number (i.e. 1, 2, 4, 7, etc.) or a threshold percentage (i.e.33%, 58%, 72%, 99%, etc.). In some aspects, the order of words, the typeof words, the importance of words, semantic variations of words,concepts of words, and/or other elements and/or techniques relating towords can be utilized for determining similarity using words. In furtheraspects, some of the words can be omitted in determining similarityusing words. Where a reference to a word is used herein it should beunderstood that a portion of a word or a collection of words can be usedinstead of or in addition to the word. In further embodiments, thedetermination may include determining that the number or percentage ofmatching features from the sub-streams of digital sound samples exceedsa threshold number (i.e. 1, 5, 17, 33, 68, 114, etc.) or a thresholdpercentage (i.e. 31%, 59%, 82%, 98%, etc.). In some aspects, the orderof features, the type of features, the importance of features, and/orother elements or techniques relating to features can be utilized fordetermining similarity using features. In other aspects, some of thefeatures can be omitted in determining similarity using features. Wherea reference to a feature is used herein it should be understood that aportion of a feature or a collection of features can be used instead ofor in addition to the feature. In further embodiments, the determinationmay include determining that the number or percentage of matching soundsamples from the sub-streams of digital sound samples exceeds athreshold number (i.e. 21, 85, 154, 297, 422, 699, etc.) or a thresholdpercentage (i.e. 29%, 48%, 69%, 96%, etc.). In some aspects, the orderof sound samples, the importance of sound samples, and/or other elementsor techniques relating to sound samples can be utilized for determiningsimilarity using sound samples. In further aspects, some of the soundsamples can be omitted in determining similarity using sound samples.Where a reference to a sound sample is used herein it should beunderstood that a collection (i.e. frame, etc.) of sound samples can beused instead of or in addition to the sound sample. In furtherembodiments, the determination may include determining that the numberor percentage of matching or substantially matching pictures of thesub-streams of digital pictures exceeds a threshold number (i.e. 28, 74,283, 322, 995, 874, etc.) or a threshold percentage (i.e. 29%, 33%, 58%,72%, 99%, etc.). In some aspects, the order of pictures, and/or otherelements or techniques relating to pictures can be utilized fordetermining similarity using pictures. In further aspects, some of thepictures can be omitted in determining similarity using pictures. Infurther embodiments, the determination may include determining that thenumber or percentage of matching features from individual picturesexceeds a threshold number (i.e. 3, 22, 47, 93, 128, 431, etc.) or athreshold percentage (i.e. 49%, 53%, 68%, 72%, 95%, etc.). In someaspects, the type of features, the importance of features, and/or otherelements or techniques relating to features can be utilized fordetermining similarity using features. In further aspects, some of thefeatures can be omitted in determining similarity using features. Infurther aspects, similarity determination can focus on features incertain regions of interest from the individual pictures. In furtheraspects, detection or recognition of persons or objects using featuresin the pictures can be utilized for determining similarity. Where areference to a feature is used herein it should be understood that aportion of a feature or a collection of features can be used instead ofor in addition to the feature. In further embodiments, the determinationmay include determining that the number or percentage of matching pixelsfrom individual pictures exceeds a threshold number (i.e. 449, 2219,92229, 442990, 1000028, etc.) or a threshold percentage (i.e. 39%, 45%,58%, 72%, 92%, etc.). In some aspects, some of the pixels can be omittedin determining similarity using pixels. In further aspects, similaritydetermination can focus on pixels in certain regions of interest fromthe individual pictures. Where a reference to a pixel is used herein itshould be understood that a collection (i.e. region, etc.) of pixels canbe used instead of or in addition to the pixel. Any combination of theaforementioned and/or other elements or techniques can be utilized inalternate embodiments. Determining comprises any action or operation byor for a Decision-making Unit 510, Substantial Similarity Comparison125, and/or other disclosed elements.

At step 6235, at least one portion of the recording of the secondconversation participant's first conversational activity is played.Playing a recording of conversational activity or portion thereof mayinclude playing sub-stream of digital pictures or portion thereof and/orsub-stream of digital sound samples or portion thereof included in therecording of conversational activity. The playing may be performedconcurrently with the user's current (i.e. first, etc.) conversationalactivity. A played sub-stream of digital pictures or portion thereof mayinclude a conversation participant's (i.e. second conversationparticipant's, etc.) visual expressions or communication. Similarly, aplayed sub-stream of digital sound samples may include a conversationparticipant's (i.e. second conversation participant's, etc.) verbalexpressions or communication. In one example, the second conversationparticipant's observing conversational activity (i.e. silent facialexpressions, silent body movements, motionless silence, etc.) or portionthereof can be played to simulate artificially intelligent conversationparticipant's (i.e. AI Conversation Participant's 55, etc.) observingwhile user speaks (i.e. performs a speaking conversational activity,etc.). In another example, the second conversation participant'sspeaking conversational activity or portion thereof can be played tosimulate artificially intelligent conversation participant's speakingwhile user observes (i.e. performs an observing conversational activity[i.e. silent facial expressions, silent body movements, motionlesssilence, etc.], etc.). In some aspects, playing can be interrupted ifthe user starts speaking and the process can redirect to step 6210. Insome embodiments, the playing may include transitioning from onesub-stream of digital pictures to another (i.e. subsequent, etc.)sub-stream of digital pictures to enable a simulated conversation to beperceived as smooth or uninterrupted, thereby enhancing user experience.Such transitioning may include manipulating one or more pictures orcontent thereof of a preceding sub-stream of digital pictures and one ormore pictures or content thereof of a subsequent sub-stream of digitalpictures. In some aspects, transitioning includes moving, centering,aligning, resizing, and/or otherwise transforming one or more picturesor content thereof of a sub-stream of digital pictures. In otheraspects, transitioning includes lighting or color adjustment of one ormore pictures or content thereof of a sub-stream of digital pictures. Infurther aspects, transitioning includes a cut, dissolve, and/or othermotion picture editing techniques between sub-streams of digitalpictures. In further aspects, transitioning includes morphing and/orother transformations of one or more pictures or content thereof ofsub-streams of digital pictures. In other embodiments, the playing mayinclude bridging between one sub-stream of digital pictures and another(i.e. subsequent, etc.) sub-stream of digital pictures to enable asimulated conversation to be perceived as smooth or uninterrupted,thereby enhancing user experience. Such bridging may include any of theaforementioned transitioning techniques, generating additional orintermediate pictures, playing or replaying pictures, and/or othertechniques. In some aspects, bridging includes interpolation,inbetweening, extrapolation, and/or other picture or frame generationtechniques. In other aspects, bridging includes playing or replaying oneor more pictures of a sub-stream of digital pictures. Playing comprisesany action or operation by or for a Decision-making Unit 510, Display21, Sound-producing Device 30, and/or other disclosed elements.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise.

A number of embodiments have been described herein. While thisdisclosure contains many specific implementation details, these shouldnot be construed as limitations on the scope of any inventions or ofwhat may be claimed, but rather as descriptions of features specific toparticular embodiments. It should be understood that variousmodifications can be made without departing from the spirit and scope ofthe invention. The logic flows depicted in the figures do not requirethe particular order shown, or sequential order, to achieve desirableresults. In addition, other or additional steps, elements, orconnections can be included, or some of the steps, elements, orconnections can be eliminated, or a combination thereof can be utilizedin the described flows, illustrations, or descriptions. Further, thevarious aspects of the disclosed devices, apparatuses, systems, and/ormethods can be combined in whole or in part with each other to produceadditional implementations. Moreover, separation of various componentsin the embodiments described herein should not be understood asrequiring such separation in all embodiments, and it should beunderstood that the described components can generally be integratedtogether in a single program or product, or packaged into multipleprograms or products. Accordingly, other embodiments are within thescope of the following claims.

The invention claimed is:
 1. A system comprising: one or moreprocessors; a memory that stores a plurality of rounds of conversationalexchange including a first round of conversational exchange, wherein thefirst round of conversational exchange includes a recording of a firstconversation participant's first conversational activity correlated witha recording of a second conversation participant's first conversationalactivity; and at least one of: a picture-capturing device configured tocapture a stream of digital pictures of a user, or a sound-capturingdevice configured to capture a stream of digital sound samples of theuser, wherein the one or more processors are configured to perform atleast: detecting the user's first conversational activity from at leastone of: the stream of digital pictures of the user, or the stream ofdigital sound samples of the user; determining at least partial matchbetween a recording of the user's first conversational activity and therecording of the first conversation participant's first conversationalactivity; and causing at least one of: a display, or a sound-producingdevice to play at least a portion of the recording of the secondconversation participant's first conversational activity, wherein thecausing is performed at least in response to the determining.
 2. Thesystem of claim 1, wherein the plurality of rounds of conversationalexchange include a second round of conversational exchange, and whereinthe second round of conversational exchange includes a recording of thefirst conversation participant's second conversational activitycorrelated with a recording of the second conversation participant'ssecond conversational activity, and wherein the one or more processorsare further configured to perform at least: detecting the user's secondconversational activity from at least one of: the stream of digitalpictures of the user, or the stream of digital sound samples of theuser; determining at least partial match between a recording of theuser's second conversational activity and the recording of the firstconversation participant's second conversational activity; and causingat least one of: the display, or the sound-producing device to play atleast a portion of the recording of the second conversationparticipant's second conversational activity, wherein the causing isperformed at least in response to the determining the at least partialmatch between the recording of the user's second conversational activityand the recording of the first conversation participant's secondconversational activity.
 3. The system of claim 1, wherein the recordingof the first conversation participant's first conversational activityincludes at least one of: a first sub-stream of a stream of digitalpictures of the first conversation participant, wherein the firstsub-stream of the stream of digital pictures of the first conversationparticipant includes the first conversation participant's visualexpressions or communication in a first part of a conversation, or afirst sub-stream of a stream of digital sound samples of the firstconversation participant, wherein the first sub-stream of the stream ofdigital sound samples of the first conversation participant includes thefirst conversation participant's verbal expressions or communication inthe first part of the conversation, and wherein the recording of thesecond conversation participant's first conversational activity includesat least one of: a first sub-stream of a stream of digital pictures ofthe second conversation participant, wherein the first sub-stream of thestream of digital pictures of the second conversation participantincludes the second conversation participant's visual expressions orcommunication in the first part of the conversation, or a firstsub-stream of a stream of digital sound samples of the secondconversation participant, wherein the first sub-stream of the stream ofdigital sound samples of the second conversation participant includesthe second conversation participant's verbal expressions orcommunication in the first part of the conversation.
 4. The system ofclaim 1, wherein the first conversation participant's firstconversational activity includes at least one of: the first conversationparticipant's speaking, the first conversation participant's silentfacial expression, the first conversation participant's silent bodymovement, the first conversation participant's motionless silence, thefirst conversation participant's absence from the conversation, or thefirst conversation participant's conversational action, and wherein thesecond conversation participant's first conversational activity includesat least one of: the second conversation participant's speaking, thesecond conversation participant's silent facial expression, the secondconversation participant's silent body movement, the second conversationparticipant's motionless silence, the second conversation participant'sabsence from the conversation, or the second conversation participant'sconversational action, and wherein the user's first conversationalactivity includes at least one of: the user's speaking, the user'ssilent facial expression, the user's silent body movement, the user'smotionless silence, the user's absence from the conversation, or theuser's conversational action.
 5. The system of claim 1, wherein theplurality of rounds of conversational exchange are included in one ormore neural networks.
 6. The system of claim 1, wherein the plurality ofrounds of conversational exchange are included in one or more graphs. 7.The system of claim 1, wherein the plurality of rounds of conversationalexchange are included in one or more sequences.
 8. The system of claim1, wherein the recording of the user's first conversational activityincludes at least one of: a first sub-stream of the stream of digitalpictures of the user, wherein the first sub-stream of the stream ofdigital pictures of the user includes the user's visual expressions orcommunication in a first part of a simulated conversation, or a firstsub-stream of the stream of digital sound samples of the user, whereinthe first sub-stream of the stream of digital sound samples of the userincludes the user's verbal expressions or communication in the firstpart of the simulated conversation.
 9. The system of claim 1, whereinthe determining the at least partial match between the recording of theuser's first conversational activity and the recording of the firstconversation participant's first conversational activity includesdetermining that a similarity between at least a portion of therecording of the user's first conversational activity and at least aportion of the recording of the first conversation participant's firstconversational activity exceeds a similarity threshold.
 10. The systemof claim 1, wherein the determining the at least partial match betweenthe recording of the user's first conversational activity and therecording of the first conversation participant's first conversationalactivity includes: determining that a number of at least partiallymatching portions of the recording of the user's first conversationalactivity and portions of the recording of the first conversationparticipant's first conversational activity exceeds a threshold number,or determining that a percentage of at least partially matching portionsof the recording of the user's first conversational activity andportions of the recording of the first conversation participant's firstconversational activity exceeds a threshold percentage.
 11. The systemof claim 10, wherein the portions of the recording of the user's firstconversational activity include at least one of: portions of a pluralityof digital pictures of the user's first conversational activity, orportions of a plurality of digital sound samples of the user's firstconversational activity, and wherein the portions of the recording ofthe first conversation participant's first conversational activityinclude at least one of: portions of a plurality of digital pictures ofthe first conversation participant's first conversational activity, orportions of a plurality of digital sound samples of the firstconversation participant's first conversational activity.
 12. The systemof claim 1, wherein the determining the at least partial match betweenthe recording of the user's first conversational activity and therecording of the first conversation participant's first conversationalactivity includes: determining that a number of at least partiallymatching words recognized from the recording of the user's firstconversational activity and words recognized from the recording of thefirst conversation participant's first conversational activity exceeds athreshold number, or determining that a percentage of at least partiallymatching words recognized from the recording of the user's firstconversational activity and words recognized from the recording of thefirst conversation participant's first conversational activity exceeds athreshold percentage.
 13. The system of claim 1, wherein the determiningthe at least partial match between the recording of the user's firstconversational activity and the recording of the first conversationparticipant's first conversational activity includes: determining that anumber of at least partially matching digital pictures from therecording of the user's first conversational activity and digitalpictures from the recording of the first conversation participant'sfirst conversational activity exceeds a threshold number, or determiningthat a percentage of at least partially matching digital pictures fromthe recording of the user's first conversational activity and digitalpictures from the recording of the first conversation participant'sfirst conversational activity exceeds a threshold percentage.
 14. Thesystem of claim 1, wherein: the first conversation participant's firstconversational activity at least partially temporally coincides with thesecond conversation participant's first conversational activity, thefirst conversation participant's first conversational activitytemporally extends the second conversation participant's firstconversational activity, or the first conversation participant's firstconversational activity temporally disjoins the second conversationparticipant's first conversational activity.
 15. The system of claim 1,wherein at least some elements of the system are included in: a singledevice, or multiple devices, and wherein the one or more processorsinclude: one or more microcontrollers, one or more computing devices, orone or more electronic devices, and wherein the plurality of rounds ofconversational exchange are included in: a knowledgebase, a knowledgestructure, or a data structure, and wherein: the user is the same as thefirst conversation participant, or the user is different than the firstconversation participant.
 16. The system of claim 1, wherein at leastsome rounds of conversational exchange of the plurality of rounds ofconversational exchange are connected by: one or more explicitconnections, or one or more implicit connections.
 17. The system ofclaim 1, wherein the first round of conversational exchange is a datastructure for storing, structuring, or organizing the recording of thefirst conversation participant's first conversational activitycorrelated with the recording of the second conversation participant'sfirst conversational activity.
 18. A system comprising: means forprocessing; and means for storing machine readable code that, whenexecuted by the means for processing, causes the means for processing toperform at least: accessing a first round of conversational exchange ofa plurality of rounds of conversational exchange, wherein the firstround of conversational exchange includes a recording of a firstconversation participant's first conversational activity correlated witha recording of a second conversation participant's first conversationalactivity; receiving at least one of: a stream of digital pictures of auser from a picture-capturing device, or a stream of digital soundsamples of the user from a sound-capturing device; detecting the user'sfirst conversational activity from at least one of: the stream ofdigital pictures of the user, or the stream of digital sound samples ofthe user; determining at least partial match between a recording of theuser's first conversational activity and the recording of the firstconversation participant's first conversational activity; and causing atleast one of: a display, or a sound-producing device to play at least aportion of the recording of the second conversation participant's firstconversational activity, wherein the causing is performed at least inresponse to the determining.
 19. The system of claim 18, wherein themeans for processing includes one or more processors, and wherein themeans for storing the machine readable code includes one or morenon-transitory machine readable media.
 20. A method implemented using acomputing system that includes one or more processors, the methodcomprising: accessing a memory that stores a plurality of rounds ofconversational exchange including a first round of conversationalexchange, wherein the first round of conversational exchange includes arecording of a first conversation participant's first conversationalactivity correlated with a recording of a second conversationparticipant's first conversational activity; receiving at least one of:a stream of digital pictures of a user from a picture-capturing device,or a stream of digital sound samples of the user from a sound-capturingdevice; detecting the user's first conversational activity from at leastone of: the stream of digital pictures of the user, or the stream ofdigital sound samples of the user; determining at least partial matchbetween a recording of the user's first conversational activity and therecording of the first conversation participant's first conversationalactivity; and playing, on at least one of: a display, or asound-producing device, at least a portion of the recording of thesecond conversation participant's first conversational activity, whereinthe playing is performed at least in response to the determining.